genetic algorithm

Open Access Article

1 - Applying genetic algorithm for automatic service identification based on quality metrics
Jan Mohammad Rajabi saeed parsa masoud bagheri ali akbar

Service-oriented architecture improves the stability and operational capability of software systems for passive defense measures. Automatic identification of services using quality of service measures ensures the successful deployment of service-oriented architecture an More

Service-oriented architecture improves the stability and operational capability of software systems for passive defense measures. Automatic identification of services using quality of service measures ensures the successful deployment of service-oriented architecture and is great importance to speed up software development life cycle. Little attention to non-functional requirements, no considerations for concurrent effects of business activities and entities and non-automated ranking of candidate services are the major issues with current approaches. The approach proposed in this paper considers both the business processes and entities, simultaneously to detect services. Applying a genetic algorithm, candidate services are identified based on quality metrics i.e. granularity, coupling, cohesion and convergence. These metrics are obtained from breaking goals to requirements of level. The TOPSIS method is applied to rank the candidate services. The illustrated case study is shown that high quality services can be identified automatically with minimal software developer’s interventions. Manuscript profile

Open Access Article

2 - Determination of Optimum SVMs Based on Genetic Algorithm in Classification of Hyper spectral Imagery
farhad samadzadegan hadise hassani

Hyper spectral remote sensing imagery, due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional More

Hyper spectral remote sensing imagery, due to its rich source of spectral information provides an efficient tool for ground classifications in complex geographical areas with similar classes. Referring to robustness of Support Vector Machines (SVMs) in high dimensional space, they are efficient tool for classification of hyper spectral imagery. However, there are two optimization issues which strongly effect on the SVMs performance: Optimum SVMs parameters determination and optimum feature subset selection. Traditional optimization algorithms are appropriate in limited search space but they usually trap in local optimum in high dimensional space, therefore it is inevitable to apply meta-heuristic optimization algorithms such as Genetic Algorithm to obtain global optimum solution. This paper evaluates the potential of different proposed optimization scenarios in determining of SVMs parameters and feature subset selection based on Genetic Algorithm (GA). Obtained results on AVIRIS Hyper spectral imagery demonstrate superior performance of SVMs achieved by simultaneously optimization of SVMs parameters and input feature subset. In Gaussian and Polynomial kernels, the classification accuracy improves by about 5% and15% respectively and more than 90 redundant bands are eliminated. For comparison, the evaluation is also performed by applying it to Simulated Annealing (SA) that shows a better performance of Genetic Algorithm especially in complex search space where parameter determination and feature selection are solve simultaneously. Manuscript profile

Open Access Article

3 - Multicast computer network routing using genetic algorithm and ant colony
Mohammad Pourmahmood Aghababa

Due to the growth and development of computer networks, the importance of the routing topic has been increased. The importance of the use of multicast networks is not negligible nowadays. Many of multimedia programs need to use a communication link to send a packet from More

Due to the growth and development of computer networks, the importance of the routing topic has been increased. The importance of the use of multicast networks is not negligible nowadays. Many of multimedia programs need to use a communication link to send a packet from a sender to several receivers. To support such programs, there is a need to make an optimal multicast tree to indicate the optimal routes from the sending source to the corresponding sinks. Providing an optimal tree for routing is a complicated problem. In this paper, we are looking forward a method for routing of multicast networks with considering some parameters such as the cost and delay. Also, this paper has emphasized the issue that every parameter in routing problem has different value for different packets. And in accordance to these parameters optimal routing multicast trees are proposed. To gain this end, the genetic algorithm and ant colony optimization approaches are adopted. The simulation results show that the presented algorithms are able to produce optimal multicast trees subject to the packets. Manuscript profile

Open Access Article

4 - Using a Hybrid PSO-GA Method for Capacitor Placement in Distribution Systems
mohammadmahdi Varahram amir mohammadi

In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but ar More

In this paper, we have proposed a new algorithm which combines PSO and GA in such a way that the new algorithm is more effective and efficient.The particle swarm optimization (PSO) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. On the other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to the initial population. This dependence to the initial population is in such a manner that the algorithm may not converge if the initial population is not well selected. This new algorithm can perform faster and does not depend on initial population and can find optimal solutions with acceptable accuracy. Optimal capacitor placement and sizing have been found using this hybrid PSO-GA algorithm. We have also found the optimal place and size of capacitors using GA and PSO separately and compared the results. Manuscript profile

Open Access Article

5 - Scheduling tasks in cloud environments using mapping framework - reduction and genetic algorithm
nima khezr nima jafari novimipour

Task scheduling is a vital component of any distributed system such as grids, clouds, and peer-to-peer networks that refer tasks to appropriate resources for execution. Common scheduling methods have disadvantages such as high time complexity, inconsistent execution of More

Task scheduling is a vital component of any distributed system such as grids, clouds, and peer-to-peer networks that refer tasks to appropriate resources for execution. Common scheduling methods have disadvantages such as high time complexity, inconsistent execution of input tasks, and increased program execution time. Exploration-based scheduling algorithms to prioritize tasks from Manuscript profile

Open Access Article

6 - Routing of Multipartite Computer Networks Using Ant Genetic Algorithm
Mohammad Pourmahmood Aghababa amin bahadorani baghbaderani

With the growth and development of computer networks, the importance of routing has become a thing of the past. The importance of using multi-sectoral networks cannot be ignored today. Many multimedia applications require sending a packet from one source to multiple des More

With the growth and development of computer networks, the importance of routing has become a thing of the past. The importance of using multi-sectoral networks cannot be ignored today. Many multimedia applications require sending a packet from one source to multiple destinations over a communication network. To support such programs, you need to create an optimal multipart tree , Which indicates the optimal routes to reach from one sender source to several desired destinations. Manuscript profile

Open Access Article

7 - The aware genetic algorithm of the best member, applied to graph coloring and metric-dimension of the graph problems
mahmood amintoosi Hashem Ezzati

Genetic algorithm is one of the most famous methods for solving Combinatorial Optimization Problems. It had various applications in different field of studies such as Electronics, Computer Science and Mathematics and still has. In this algorithm, the population members More

Genetic algorithm is one of the most famous methods for solving Combinatorial Optimization Problems. It had various applications in different field of studies such as Electronics, Computer Science and Mathematics and still has. In this algorithm, the population members which contribute for producing the next generation are selected according to their fitness values. The combination of the members is through Crossover Operator; And in some versions a few of the best members migrate to the next generation directly. Normally, the weak members of population may participate to the next generation. In this study, the combination operators are aware of the best member of generation; Only those child which are as good as the best member, are allowed to form the next generation. The proposed method is applied on graph coloring and finding metric-dimension of graph problems. The results are compared with the common genetic algorithm. Experimental results shows the superior performance of the proposed method in comparison to common genetic algorithm. Manuscript profile

Open Access Article

8 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
Sara Motamed Ali Broumandnia Azam sadat Nourbakhsh

10.7508/jist.2013.02.002

Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system More

Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features. Manuscript profile

Open Access Article

9 - A Hybrid Cuckoo Search for Direct Blockmodeling
Saeed NasehiMoghaddam mehdi ghazanfari babak teimourpour

10.7508/jist.2017.18.001

As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within posit More

As a way of simplifying, size reducing and making sense of the structure of each social network, blockmodeling consists of two major, essential components: partitioning of actors to equivalence classes, called positions, and clarifying relations between and within positions. Partitioning of actors to positions is done variously and the ties between and within positions can be represented by density matrices, image matrices and reduced graphs. While actor partitioning in classic blockmodeling is performed by several equivalence definitions, such as structural and regular equivalence, generalized blockmodeling, using a local optimization procedure, searches the best partition vector that best satisfies a predetermined image matrix. The need for known predefined social structure and using a local search procedure to find the best partition vector fitting into that predefined image matrix, makes generalized blockmodeling be restricted. In this paper, we formulate blockmodel problem and employ a genetic algorithm to search for the best partition vector fitting into original relational data in terms of the known indices. In addition, during multiple samples and various situations such as dichotomous, signed, ordinal or interval valued relations, and multiple relations the quality of results shows better fitness to original relational data than solutions reported by researchers in classic, generalized, and stochastic blockmodeling field. Manuscript profile

Open Access Article

10 - Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms
Amin Kamalinia Ali Ghaffari

10.7508/jist.2016.04.008

Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cl More

Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The results of simulation and statistical analysis of proposed scheme indicate that the proposed algorithm, when compared with three other heuristic and a memetic algorithms, has optimized the makespan required for executing tasks. Manuscript profile

Open Access Article

11 - Toward Energy-Aware Traffic Engineering in Intra-Domain IP Networks Using Heuristic and Meta-Heuristics Approaches
Muharram Mansoorizadeh

10.7508/jist.2018.02.005

Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization i More

Because of various ecological, environmental, and economic issues, energy efficient networking has been a subject of interest in recent years. In a typical backbone network, all the routers and their ports are always active and consume energy. Average link utilization in internet service providers is about 30-40%. Energy-aware traffic engineering aims to change routing algorithms so that low utilized links would be deactivated and their load would be distributed over other routes. As a consequence, by turning off these links and their respective devices and ports, network energy consumption is significantly decreased. In this paper, we propose four algorithms for energy-aware traffic engineering in intra-domain networks. Sequential Link Elimination (SLE) removes links based on their role in maximum network utilization. As a heuristic method, Extended Minimum Spanning Tree (EMST) uses minimum spanning trees to eliminate redundant links and nodes. Energy-aware DAMOTE (EAD) is another heuristic method that turns off links with low utilization. The fourth approach is based on genetic algorithms that randomly search for feasible network architectures in a potentially huge solution space. Evaluation results on Abilene network with real traffic matrix indicate that about 35% saving can be obtained by turning off underutilized links and routers on off-peak hours with respect to QoS. Furthermore, experiments with GA confirm that a subset of links and core nodes with respect to QoS can be switched off when traffic is in its off-peak periods, and hence energy can be saved up to 37%. Manuscript profile

Open Access Article

12 - Handwritten Digits Recognition Using an Ensemble Technique Based on the Firefly Algorithm
Azar Mahmoodzadeh Hamed Agahi Marzieh Salehi

10.7508/jist.2018.03.003

This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminat More

This paper develops a multi-step procedure for classifying Farsi handwritten digits using a combination of classifiers. Generally, the technique relies on extracting a set of characteristics from handwritten samples, training multiple classifiers to learn to discriminate between digits, and finally combining the classifiers to enhance the overall system performance. First, a pre-processing course is performed to prepare the images for the main steps. Then three structural and statistical characteristics are extracted which include several features, among which a multi-objective genetic algorithm selects those more effective ones in order to reduce the computational complexity of the classification step. For the base classification, a decision tree (DT), an artificial neural networks (ANN) and a k-nearest neighbor (KNN) models are employed. Finally, the outcomes of the classifiers are fed into a classifier ensemble system to make the final decision. This hybrid system assigns different weights for each class selected by each classifier. These voting weights are adjusted by a metaheuristic firefly algorithm which optimizes the accuracy of the overall system. The performance of the implemented approach on the standard HODA dataset is compared with the base classifiers and some state-of-the-art methods. Evaluation of the proposed technique demonstrates that the proposed hybrid system attains high performance indices including accuracy of 98.88% with only eleven features. Manuscript profile

Open Access Article

13 - Sailor Localization in Oceans Beds using Genetic and Firefly Algorithm
Shruti Gupta Dr Ajay Rana Vineet Kansal

10.29252/jist.8.31.175

The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the More

The Localization is the core element in Wireless Sensor Network WSN, especially for those nodes without GPS or BDS; leaning towards improvement, based on its effective and increased use in the past decade. Localization methods are thus very important for estimating the position of relative nodes in the network allowing a better and effective network for increasing the efficiency and thus increasing the lifeline of the network. Determining the current limitations in FA that are applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinity of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. This paper presents GEFIR (GenFire) algorithm to calculate position of unknown nodes for the fishermen in the ocean. The proposed approach calculates the position of unknown nodes, the proposed method effectively selects the anchor node in the cluster head to reduce the energy dissipation. Major benefits over other similar localization algorithms are a better positioning of nodes is provided and average localization error is reduced which eventually leads to better efficiency thus optimize the lifetime of the network for sailors. The obtained results depict that the proposed model surpasses the previous generation of localization algorithm in terms of energy dispersion and location estimation which is suitable for fishermen on the ocean bed. Manuscript profile

Open Access Article

14 - Reducing Energy Consumption in Sensor-Based Internet of Things Networks Based on Multi-Objective Optimization Algorithms
Mohammad sedighimanesh Hessam Zandhessami Mahmood Alborzi Mohammadsadegh Khayyatian

10.52547/jist.15639.10.39.180

Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The mos More

Energy is an important parameter in establishing various communications types in the sensor-based IoT. Sensors usually possess low-energy and non-rechargeable batteries since these sensors are often applied in places and applications that cannot be recharged. The most important objective of the present study is to minimize the energy consumption of sensors and increase the IoT network's lifetime by applying multi-objective optimization algorithms when selecting cluster heads and routing between cluster heads for transferring data to the base station. In the present article, after distributing the sensor nodes in the network, the type-2 fuzzy algorithm has been employed to select the cluster heads and also the genetic algorithm has been used to create a tree between the cluster heads and base station. After selecting the cluster heads, the normal nodes become cluster members and send their data to the cluster head. After collecting and aggregating the data by the cluster heads, the data is transferred to the base station from the path specified by the genetic algorithm. The proposed algorithm was implemented with MATLAB simulator and compared with LEACH, MB-CBCCP, and DCABGA protocols, the simulation results indicate the better performance of the proposed algorithm in different environments compared to the mentioned protocols. Due to the limited energy in the sensor-based IoT and the fact that they cannot be recharged in most applications, the use of multi-objective optimization algorithms in the design and implementation of routing and clustering algorithms has a significant impact on the increase in the lifetime of these networks. Manuscript profile

Open Access Article

15 - A Task Mapping and Scheduling Algorithm based on Genetic Algorithm for Embedded System Design
mohadese nikseresht Mohsen Raji

Embedded system designers face numerous design requirements and objectives (such as runtime, power consumption and reliability). Since meeting one of these requirements mostly contradicts other design requirements, it seem to be inevitable to apply multi-objective appr More

Embedded system designers face numerous design requirements and objectives (such as runtime, power consumption and reliability). Since meeting one of these requirements mostly contradicts other design requirements, it seem to be inevitable to apply multi-objective approaches in various stages of designing embedded systems, including task scheduling step. In this paper, a multi-objective task mapping and scheduling in the design stage of the embedded system is presented. In this method, tasks are represented by task graphs assuming that the hardware architecture platform is given and determined. In order to manage the dependencies between tasks in the task graph, a segmentation method is used, in which the tasks that can be executed simultaneously are specified in a segment and is considered in the scheduling process. In the proposed method, the task mapping and scheduling problem is modeled as a genetic algorithm-based multi-objective optimization problem considering execution time, energy consumption, and reliability. In comparison to similar previous works, the proposed scheduling method respectively provides 21.4%, 19.2%, and 20% improvement in execution time, energy consumption, and reliability. Applying a multi-objective helps the designer to pick out the best outcome according to different considerations. Manuscript profile

Open Access Article

16 - Presenting the model for opinion mining at the document feature level for hotel users' reviews
ELHAM KHALAJJ shahriyar mohammadi

Nowadays, online review of user’s sentiments and opinions on the Internet is an important part of the process of people deciding whether to choose a product or use the services provided. Despite the Internet platform and easy access to blogs related to opinions in the More

Nowadays, online review of user’s sentiments and opinions on the Internet is an important part of the process of people deciding whether to choose a product or use the services provided. Despite the Internet platform and easy access to blogs related to opinions in the field of tourism and hotel industry, there are huge and rich sources of ideas in the form of text that people can use text mining methods to discover the opinions of. Due to the importance of user's sentiments and opinions in the industry, especially in the tourism and hotel industry, the topics of opinion research and analysis of emotions and exploration of texts written by users have been considered by those in charge. In this research, a new and combined method based on a common approach in sentiment analysis, the use of words to produce characteristics for classifying reviews is presented. Thus, the development of two methods of vocabulary construction, one using statistical methods and the other using genetic algorithm is presented. The above words are combined with the Vocabulary of public feeling and standard Liu Bing classification of prominent words to increase the accuracy of classification Manuscript profile

Open Access Article

17 - Relief Operation Management in an Emergency by Covering Tour Problem concept and possibility of Direct Operation
حسین جمالی مهدی بشیری رضا توکلی مقدم

One of the concepts that are presented in most of areas is sustainable management where human, social and environmental aspects are considered besides of economical impacts. One of the social aspects is agility of relief operations in emergency situations. To achieve ag More

One of the concepts that are presented in most of areas is sustainable management where human, social and environmental aspects are considered besides of economical impacts. One of the social aspects is agility of relief operations in emergency situations. To achieve agile management in such circumstances, a generalized model of the capacitated covering tour problem with the possibility of direct aid to the affected areas with hard time windows is provided in this study. The injured areas might be covered by relief centers or can be operated by a direct relief from the central station by special vehicles. To improve the model and management the time, constraints of hard time windows have been added to the model. This would prioritize the management of relief operations in injured areas due to the severity of the damage on them. The purpose of this study is to determine the optimal set of established relief centers, distribute rescue teams and relief aid from the central station of the bases, and also deploy relief teams to the disaster that is not covered by any relief center in optimized cost and time. A mixed integer programming model is developed and a genetic algorithm is proposed to solve the model in large and medium instances. The results of numerical examples confirm the validity and effectiveness of the genetic algorithm. Manuscript profile

Open Access Article

18 - Solve the issue of project scheduling in a steady state with resource constraints and timing of delivery intervals
Meysam Jafari Eskandari rozbeh azizmohammadi

Due to considering the real conditions of project and solving manager’s problems, the primary methods development of scheduling for projects has recently drawn researcher’s attentions so that these methods are looking for finding optimal sequence to realize project goal More

Due to considering the real conditions of project and solving manager’s problems, the primary methods development of scheduling for projects has recently drawn researcher’s attentions so that these methods are looking for finding optimal sequence to realize project goals and to provide its constraints such as dependence, resource constraint (renewable and non-renewable). The importance of these issues has practically and theoretically led researchers to do much efforts on different conditions of issues for project schedule, various methods to solve and or to develop each of them. In this research based on selection of some executive methods for any activity with renewable and non-renewable resource and considering prerequisite relation from kind of start to end and having delivery time for any activity in two time periods that has been provided with regard to delivery time of penalty cost with delay or without delay. The presented model has been solved in small scale by gams software and in small, medium and large scale using meta-heuristic Methods of NSGA ll and cuckoo after coding in software of matlab 2013. The comparison of the answer obtained from the above algorithm indicates the better performance of genetic algorithm in most indexes and cuckoo algorithm has superiority on time index of problem solving. Manuscript profile

Open Access Article

19 - A New Evolutionary Estimation of Distribution Algorithm Based on Learning Automata
M. R. Meybodi

In order to overcome the poor behaviors of genetic algorithms in some problems other classes of evolutionary algorithms have been recently developed by researchers. Although these algorithms do not have the simplicity of classic genetic algorithms but they are superior More

In order to overcome the poor behaviors of genetic algorithms in some problems other classes of evolutionary algorithms have been recently developed by researchers. Although these algorithms do not have the simplicity of classic genetic algorithms but they are superior to genetic algorithms. The Probabilistic Model Building Genetic Algorithms or Estimation of Distribution Algorithms (EDAs) is one of these classes which is recently developed. In this paper we introduce a new estimation of distribution algorithm based on Learning Automata. The proposed algorithm is a model based search optimization method that uses a set of learning automata as a probabilistic model of the population of solutions in the search space. The proposed algorithm is a simple algorithm which has produced good results for the optimization problems considered in this problem. Manuscript profile

Open Access Article

20 - New Optimization Approach in the Design of Yagi Uda Antenna
A. A. Lotfi-Neyestanak F. Hojjat Kashani

In this paper, several methods for optimization of a 5-elements Yagi antenna are proposed using genetic algorithm, genetic algorithm inspired by simulated annealing, genetic algorithm based on fuzzy decision making, and particle swarm method. High speed run time of Supe More

In this paper, several methods for optimization of a 5-elements Yagi antenna are proposed using genetic algorithm, genetic algorithm inspired by simulated annealing, genetic algorithm based on fuzzy decision making, and particle swarm method. High speed run time of SuperNEC software, it has been used for analyzing the presented methods. The use of genetic algorithm or genetic algorithm inspired by simulated annealing for antenna optimization in a specific frequency band, needs long run time. Besides, reduction of the number of population and the amount of repetition, causes decrease in optimization precision. So, an optimization system base on fuzzy decision making is proposed. In addition, the particle swarm method which has a good convergence rate and good performance has been proposed to obtain a better optimization. The comparison between the proposed optimization methods shows that the genetic based on fuzzy decision making and the particle swarm methods have the best performance and functionality and the least run time. Manuscript profile

Open Access Article

21 - Missile Autopilot Design Using Fuzzy Gain-Scheduling
A. Akbarzadeh Kalat H. R. Momeni

In this paper a controller using fuzzy gain-scheduling for the channels of a tactical missile is designed such that in flight trajectories, performance is achieved. In this design method, the fuzzy gain-scheduling zone centers are determined by a training algorithm acco More

In this paper a controller using fuzzy gain-scheduling for the channels of a tactical missile is designed such that in flight trajectories, performance is achieved. In this design method, the fuzzy gain-scheduling zone centers are determined by a training algorithm according to dynamic pressure, Mach number and coefficients of linear model of system in major operating points. The fuzzy system is learned using combined genetic and linear least squares algorithms. In this manner both global optimum solution and fast convergence are reachable. Moreover the membership functions in fuzzy inference system are chosen with special and suitable properties, which cause simple and effective scheduling process. Performance of this method is shown with case study simulation result. Manuscript profile

Open Access Article

22 - Determination of Control Variables in Power Systems to Maximum Load Restoration
H. Afrakhte A. Yazdian Varjani

This paper presents a new method to maximize load restoration in faulted condition in power systems. Control variables which are used to restore maximum load include tap of power transformers, generation rescheduling, and load shedding in the worst case. Modeling is don More

This paper presents a new method to maximize load restoration in faulted condition in power systems. Control variables which are used to restore maximum load include tap of power transformers, generation rescheduling, and load shedding in the worst case. Modeling is done in three stages with various control variables arrangements. In the first stage of modeling, power transformer tap is used as a control variable. In the second stage, power transformers taps and generations rescheduling are considered. In the last stage, load shedding as another variable is added to decision variable spaces. Since the number of variables is high and final solution space can be nonlinear, genetic algorithm is used in the optimization process. The capabilities of the proposed method were assessed using IEEE-RTS test system with satisfactory results. Manuscript profile

Open Access Article

23 - Analyzing Weighted Attack Graphs Using Genetic Algorithms
M. Abadi Saeed Jalili

Each attack graph represents a collection of possible attack scenarios in a computer network. In this paper, we use weighted attack graphs (WAGs) for vulnerability assessment of computer networks. In these directed graphs, a weight is assigned to each exploit by the sec More

Each attack graph represents a collection of possible attack scenarios in a computer network. In this paper, we use weighted attack graphs (WAGs) for vulnerability assessment of computer networks. In these directed graphs, a weight is assigned to each exploit by the security analyst. The weight of an exploit is proportionate to the cost required to prevent that exploit. The aim of analyzing a weighted attack graph is to find a critical set of exploits such that the sum of their weights is minimum and by preventing them no attack scenario is possible. In this paper, we propose a greedy algorithm, a genetic algorithm with a greedy mutation operator, and a genetic algorithm with a dynamic fitness function for analyzing the weighted attack graphs. The proposed algorithms are used to analyze a sample weighted attack graph and several randomly generated large-scale weighted attack graphs. The results of experiments show that the proposed genetic algorithms outperform the greedy algorithm and find a critical set of exploits with less total weight. Finally, we compare the performance of the second genetic algorithm with an approximation algorithm for analyzing several randomly generated large-scale simple attack graphs. The results of experiments show that our proposed genetic algorithm has better performance than the approximation algorithm and finds a critical set of exploits with less cardinality. Manuscript profile

Open Access Article

24 - Combined Subtransmission Substation and Network Expansion Planning Using Genetic Algorithm, Ant Colony algorithm, and hybrid Ant Colony and Genetic Algorithm
V. Amir H. Seifi S. M. Sepasian g. r. yousefi

This research presents new algorithms for subtransmission simultaneous substation and network expansion planning. Given an existing system model, the projected load growth in a target year and various system expansion options, the algorithms find the optimal mix of sys More

This research presents new algorithms for subtransmission simultaneous substation and network expansion planning. Given an existing system model, the projected load growth in a target year and various system expansion options, the algorithms find the optimal mix of system expansion options to minimize the cost function subject to various system constraints and single contingencies on lines and transformers. The system expansion options considered include building new subtransmission lines/substations, the capacity to be upgraded and the service area of HV/MV substations. In this research, Genetic Algorithm (GA) with new coding, Ant Colony algorithm (AC) and hybrid Ant Colony and Genetic Algorithm (AC&GA) methods are employed. The optimization results are compared with successive elimination method to demonstrate the performance improvement. Manuscript profile

Open Access Article

25 - Investigation and Evaluation of Adaptive Nulling Methods of Array Antennas Using Genetic Algorithm
S. Jam M. Delroshan

This paper describes an approach to adaptive nulling with phased arrays. A genetic algorithm adjusts some of the least significant bits of the beam steering phase-shifters to minimize the total output power of the array. Also, some other criterions such as Mean Square E More

This paper describes an approach to adaptive nulling with phased arrays. A genetic algorithm adjusts some of the least significant bits of the beam steering phase-shifters to minimize the total output power of the array. Also, some other criterions such as Mean Square Error and Signal to Interference plus Noise Ratio are used and compared with each other. Using the least significant bits results in small perturbation in the main beam of the radiation pattern and puts the nulls in the direction of the interferences. Double search and weighted mutation are used to reduce the complexity of the algorithm. Also, the performance of genetic algorithm is compared with MPDR which is an optimum technique for beamforming. Finally, it is shown that the genetic algorithm performs superior to MPDR. Manuscript profile

Open Access Article

26 - Blind Modulation Recognition of Communication Signals Based on Support Vector Machines
S. Shaerbaf M. Khademi Mohammad Molavi

Automatic modulation type classifier is a system which recognizes the modulation type of received signal automatically from some possible, pre-assumed types. Automatic modulation classification has applications such as spectrum surveillance, signal confirmation, interfe More

Automatic modulation type classifier is a system which recognizes the modulation type of received signal automatically from some possible, pre-assumed types. Automatic modulation classification has applications such as spectrum surveillance, signal confirmation, interference identification, software radio, etc. This paper, proposes a new method for recognition of 9 famous digital and analog modulations, which no need for prior knowledge of the signal to be recognized. This system is used to separate AM, FM, DSB and SSB in Analog modulations and 2ASK, 2PSK, 2FSK, 4PAM and 16QAM in digital modulations. Support Vector Machines (SVM) is used to classify these modulations and Genetic Algorithm is used to optimize Classifier Structure. Simulation results show that proposed algorithms have a good performance in comparison with other algorithms. Computational simplicity, High training speed and High classification rate, are the advantages of proposed algorithms. Manuscript profile

Open Access Article

27 - Optimal Bidding in Electricity Market Using Game Theory
N. Baei M. Parsa-Moghaddam

This paper, presents a new approach for bidding strategy in spot electricity markets. A two-level optimization method is used for profit maximization of non-cooperative firms, while taking into account overall system constrains. In this approach, the market equilibrium More

This paper, presents a new approach for bidding strategy in spot electricity markets. A two-level optimization method is used for profit maximization of non-cooperative firms, while taking into account overall system constrains. In this approach, the market equilibrium points are determined as Nash Equilibria. In order to capture the behavior of all market participants and therefore, a much more competitive environment both the suppliers and consumers are considered as the players of the market. To avoid local maxima solutions, Genetic Algorithm based optimization is incorporated. The proposed method has been applied to IEEE 9 bus system with satisfactory results. Manuscript profile

Open Access Article

28 - Memetic Algorithm for Economic Dispatch with Nonsmooth Cost Functions
M. Neyestani M. M. Farsangi H. Nezamabadi-pour

This paper presents a new approach to economic dispatch (ED) problems with nonconvex cost functions using Memetic Algorithm (MA). The practical ED problem have nonconvex cost functions with equality and inequality constraints that make the problem of finding the global More

This paper presents a new approach to economic dispatch (ED) problems with nonconvex cost functions using Memetic Algorithm (MA). The practical ED problem have nonconvex cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. In this paper, MA with three different local searches is suggested to deal with the equality and inequality constraints in the ED problem. To validate the results obtained by proposed MAs, a Real Genetic Algorithm (RGA) and an MA adopted from the literature are applied for comparison. Also, the results obtained by MAs and RGA are compared with the previous approaches reported in the literature. The results show that the MAs produce optimal or nearly optimal solutions for all study systems. Manuscript profile

Open Access Article

29 - GA-Based Optimized UPFC Controller for Improving Damping of Oscillations in Power Systems
S. A. Taher R. Hematti A. Abdolalipour

In this paper, the use of the supplementary controller of a Unified Power Flow Controller (UPFC) to improve damping of oscillations in Single Machine Infinite Bus (SMIB) system is investigated. The controller was designed based on a linearized modified Phillips Heffron More

In this paper, the use of the supplementary controller of a Unified Power Flow Controller (UPFC) to improve damping of oscillations in Single Machine Infinite Bus (SMIB) system is investigated. The controller was designed based on a linearized modified Phillips Heffron model of SMIB in state space form. In practice systems use simple Proportional Integral (PI) controllers to control UPFC. However, since the PI control parameters are usually tuned based on classical or trial-and-error approaches, they are incapable of obtaining a good dynamic performance for a wide range of operation conditions. To address this problem, in this research an optimization approach, based on the Genetic Algorithms (GA) method is proposed for the design of UPFC controller (supplementary damping controller) for increasing damping of power system oscillations is developed. Several linear and nonlinear time-domain simulation tests clearly show the effectiveness and validity of the proposed method in enhancing of oscillations257-260damping. Comparisons between the performances of both the proposed and conventional supplementary controllers are made. Computer test results show that proposed method is very effective in oscillations damping and in the meantime is more robust than its conventional counterpart. Manuscript profile

Open Access Article

30 - SVD-Based Adaptive Multiuser Detection for Optimized Chaotic DS-CDMA Systems
S. Shaerbaf S. A. Seyedin

In recent years, chaotic signals have created a new area in the designation of wideband communication systems. Most of the activity has focused on DS-CDMA systems, in which the conventional pseudo-noise sequences will be replaced by binary chaotic sequences. Unfortunat More

In recent years, chaotic signals have created a new area in the designation of wideband communication systems. Most of the activity has focused on DS-CDMA systems, in which the conventional pseudo-noise sequences will be replaced by binary chaotic sequences. Unfortunately, despite the advantages of chaotic systems such as aperiodicity, low cost generation and noise-like spectrum, the performance of most of such designs is not still suitable for multiuser wireless channels. In this paper, we propose a novel method based on singular value decomposition for adaptive multiuser detection in chaos-based DS-CDMA systems. We also propose a new genetic algorithm-based method for the optimal generation of chaotic sequences in such systems. Simulation results show that our proposed nonlinear receiver with optimized chaotic sequences outperforms the conventional DS-CDMA systems with “maximal length” codes as well as non-optimized chaos-based DS-CDMA systems in all channel condition, particularly for under-loaded CDMA condition, which the number of active users is less than processing gain. Manuscript profile

Open Access Article

31 - An Intelligent BGSA Based Method for Feature Selection in a Persian Handwritten Digits Recognition System
N. Ghanbari S. M. Razavi S. H. Nabavi Karizi

In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, us More

In this paper, an intelligent feature selection method for recognition of Persian handwritten digits is presented. The fitness function associated with the error in the Persian handwritten digits recognition system is minimized, by selecting the appropriate features, using binary gravitational search algorithm. Implementation results show that the use of intelligent methods is well able to choose the most effective features for this recognition system. The results of the proposed method in comparison with other similar methods based on genetic algorithm and binary particle method of optimizing indicates the effective performance of the proposed method. Manuscript profile

Open Access Article

32 - A New Formulation for the Probabilistic Congestion Management Using Chance Constrained Programming
M. Hojjat M. H. Javidi

In this paper, a new method for probabilistic congestion management considering power system uncertainties is proposed. Chance constrained programming (CCP) is used to formulate the probabilistic congestion management as an efficient approach for stochastic optimization More

In this paper, a new method for probabilistic congestion management considering power system uncertainties is proposed. Chance constrained programming (CCP) is used to formulate the probabilistic congestion management as an efficient approach for stochastic optimization problems. The CCP based probabilistic congestion management is solved utilizing a numerical approach by applying the Monte-Carlo technique into the real-coded genetic algorithm. The effectiveness of the proposed method is evaluated applying the method to the modified IEEE 9-bus test system. The results of the proposed approach are compared with those of the expected method to have a comprehensive study. The simulation results reflect the flexibility of the proposed approach in transmission congestion management. Manuscript profile

Open Access Article

33 - Design Improvement of Synchronous Reluctance Motor Geometry, Using Neural-Network, Genetic Algorithm and Finite Element Method
M. Haghparast S. Taghipour Boroujeni A. Kargar

appropriate approach to reach high efficiency in Synchronous Reluctance (SynRel) machines is to enhance these machines’ magnetic saliency. This is usually done by changing the geometry of machine and especially by changing the number and shape of rotor flux barriers. In More

appropriate approach to reach high efficiency in Synchronous Reluctance (SynRel) machines is to enhance these machines’ magnetic saliency. This is usually done by changing the geometry of machine and especially by changing the number and shape of rotor flux barriers. In this paper an intelligent- method have been used to optimizing the design of SynRel motors based on magnetic saliency ratio. To achieve this aim, all of the motor parameters including stator geometry, axial length of machine, winding type, and number of flux barriers in rotor are assumed constant and just position of the rotor flux barriers are optimized. These positions have been defined by six parameters. Changing these parameters, the magnetic saliency of machine is calculated by finite element analysis (FEA). Using these values to train a neural network (NN), a modeling function is obtained for magnetic saliency of SynRel machine. Considering this NN as the target function in genetic algorithm (GA), the parameters of SynRel machine have been optimized and the best rotor structure with highest magnetic saliency has been obtained. Finally the abilities of NN in correct estimation of magnetic saliency and motor synchronization were approved by FEA and dynamic simulation. Manuscript profile

Open Access Article

34 - Increment of Distributed Generation Penetration in Distribution Networks Using simultaneous Placement of Distributed Generation Resources and Energy Storage Systems
N. Biabani M. Ramezani H. Falaghi

In addition to the great benefits of distributed generation (DG) resources to power systems, there are some disadvantages. In spite of some benefits like decrease of received power from transmission grid, increment of DGs penetration can lead to voltage increase in dist More

In addition to the great benefits of distributed generation (DG) resources to power systems, there are some disadvantages. In spite of some benefits like decrease of received power from transmission grid, increment of DGs penetration can lead to voltage increase in distribution networks during off peak. Hence the recent efforts have been made to handle problems to increase the penetration of these resources. The use of energy storage systems (ESSs) is one of the ways to prevent defects may arise from use of DGs in power systems and can help to increase penetration of DGs in power systems. ESSs can save energy during off peak and deliver it to the network in peak hours; hence, these equipment can reduce power losses and prevent voltage deviation during off peak by increasing load due to ESS charging. In this paper, first DG allocation and ESS placement are introduced then simultaneous placement of DG and ESS to reduce power losses in the distribution network are described. The proposed models are solved using genetic algorithm as optimization tool. The obtained results show that simultaneous placement could increase DG penetration compared to the separate allocation of these devices and pro Manuscript profile

Open Access Article

35 - The Probabilistic Small Signal Stability Analysis and Coordinate Tuning of PSSs and TCSC in the Power System with Considering the Wind Farm Generation Uncertainty
H. Ahmadi H. Seifi

With the decreasing of the fossil fuels and increasing of the environmental pollution, using of renewable energy resources is growing rapidly. On the other hand, the restructured electricity industry causes to cooperation of the distributed generation resources in the c More

With the decreasing of the fossil fuels and increasing of the environmental pollution, using of renewable energy resources is growing rapidly. On the other hand, the restructured electricity industry causes to cooperation of the distributed generation resources in the competitive electricity market. In such situation, the presence of the wind farms in the power system in order to provide the system loads is quite favorable. However, wind farm generation depends on the wind speed and the uncertainty in the generation cause to some concerns about the connection and operation of the power system. So, in this paper, a probabilistic approach for small signal stability analysis with considering the wind farm generation uncertainty based on PCM method is proposed. The PCM method is based on the orthogonal polynomials which provide a linear model for desired output. The continuous changes of the wind farm generation level cause to variation on the operating point that the control equipment parameters should be adjusted based on the new operation conditions. Therefore, genetic algorithm and the approximate models which are obtained from the PCM method are used. In order to validate the proposed method, the IEEE 10-machine and IEEE 16-machine test system are used. Manuscript profile

Open Access Article

36 - Application of Epsilon Variable-Multi Objective Genetic Algorithm for Multi-Objective Optimal Power Flow with TCSC
E. Afzalan M. Joorabian

This paper ε-multi objective genetic algorithm variable (εV-MOGA) to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem More

This paper ε-multi objective genetic algorithm variable (εV-MOGA) to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FACTS devices considered include thyristor controlled series capacitor (TCSC). The proposed approach has been examined and tested on the modified IEEE 57-bus test system. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, multi-objective differential evolution. Manuscript profile

Open Access Article

37 - Coordinated Framework for Reconfiguration and Direct Load Control to Meet the Challenges of Distribution Systems Operation
E. Hosseini Mohammad Sadegh Sepasian H. Arasteh V. Vahidinasab

The basic approach of this paper is to improve the operational condition of distribution systems by the simultaneous utilization of system reconfiguration and direct load control programs. A Genetic Algorithm (GA) based algorithm is employed to find the optimal states o More

The basic approach of this paper is to improve the operational condition of distribution systems by the simultaneous utilization of system reconfiguration and direct load control programs. A Genetic Algorithm (GA) based algorithm is employed to find the optimal states of switches as well as the optimal incentives of the demand response programs. The concept of price elasticity of demand is utilized to illustrate the changes of electricity consumption pattern as a result of customers’ participation in Demand Response (DR). The objective function of the proposed model is network operation costs. In addition, voltage constraints, lines capacity limits and the related constraints of DR programs are considered in the optimization problem. Finally, the effectiveness of the proposed method in reducing operation costs is shown using the 33-bus distribution network. The simulation results show that the coordination of reconfiguration and DR can reduce the operation costs and load shedding requirements in addition to solving lines’ over loading problems. Manuscript profile

Open Access Article

38 - Design, Optimization, and Finite Element Analysis of a Disk-Type Permanent Magnet Synchronous Motor
S. A. Seyedi Seadati A. Halvaei Niasar

This paper proposes to design, optimization and finite element simulation of an axial-flux, super-high speed, permanent magnet motor. The target motor with 0.5 hp rated power at speed of 60,000 rpm is used in a special industrial application. Based on nominal specificat More

This paper proposes to design, optimization and finite element simulation of an axial-flux, super-high speed, permanent magnet motor. The target motor with 0.5 hp rated power at speed of 60,000 rpm is used in a special industrial application. Based on nominal specifications of the motor and using analytical relations of motor design, the design calculations, sizing and motor dimensions are investigated. Due to special application of the target motor that needs to the demanded torque with minimum current and copper losses, the dimensions and design specifications of motor is optimized via genetic algorithm based on a torque per ampere cost function. Optimization algorithm determines the optimum value of airgap, permanent magnet flux density, current density and turns number of stator windings. To demonstrate of analytical design and optimization results, using 3-D model of motor in Maxwell software, finite element analysis are carried out in Magneto-static and Transient modes. The FEM simulation results confirm the analytical design results. Moreover, they show the significant reduction in RMS current and copper loss at rated torque. There is a good agreement between the values of torque, motor efficiency, and flux density resulted from both methods. Manuscript profile

Open Access Article

39 - Green Routing Protocol Based on Sleep Scheduling in Mobile Ad-Hoc Network
Z. Movahedi A. Karimi

Over recent years, green communication technology has been emerged as an important area of concern for communication research and industrial community. The reason of paying attention of this area is its effect on reducing environmental pollutions. According to recent re More

Over recent years, green communication technology has been emerged as an important area of concern for communication research and industrial community. The reason of paying attention of this area is its effect on reducing environmental pollutions. According to recent research, a significant share of these pollutions is produced by the local area computer networks. A mobile ad-hoc network (MANET) is one of the widely used local area networks. The energy efficiency is important in MANETs not only from the green communication point of view, but also due to the network limitations in terms of battery lifetime. Of course, MANETs characterization such as distributed nature and lack of administration, nodes mobility, frequent topology changes and scare resources makes the greening trend a challenging task in such a context. In this paper, we propose and implement a green routing protocol for MANET which solves the idle energy consumption by allowing the necessary nodes and switching off the other un-utilized nodes. Simulation results show this can help to the 20 percentage of saving energy in the environment on average and also aware of the quality of service. Manuscript profile

Open Access Article

40 - Improving Security of LSBM Steganography Using of Genetic Algorithm, Mmulti-Key and Blocking
vajiheh sabeti Sepide faiazi hadise shirinkhah

By increasing the precision of steganalysis attacks in discovering methods of steganography, the need to improve the security of steganographic methods is felt more than ever. The LSBM is one of the simplest methods of steganography, which have been proposed relatively More

By increasing the precision of steganalysis attacks in discovering methods of steganography, the need to improve the security of steganographic methods is felt more than ever. The LSBM is one of the simplest methods of steganography, which have been proposed relatively successful attacks for its discovery. The main purpose of this paper is to provide a method for improving security of LSBM. The choice of the sequence of pixels to embed and how to modify them varies in LSBM-based methods. In most existing methods some of these decisions are made at random. In the proposed method in this paper, a multi-key idea in the first step and a genetic algorithm in the second step are used to make better decisions. In the proposed method, as MKGM, the image is blocked and GLSBM is executed for each block with different keys and finally the block with the least histogram change compared to the original block is included in the stego image. The GLSBM method is the same as the LSBM method except that the genetic algorithm is used to decide whether to increase or decrease non-matching pixels. Comparison of the image quality criteria and the accuracy of the attacks in the detection of the proposed method show that these criteria are improved compared to the original LSBM method. Manuscript profile

Open Access Article

41 - Optimizing the Selection and Composition of QoS-Aware Web Services by Considering Dependency, Conflict, and Correlation between Web Services
mahdi farzandway F. Shams

Today, the continuous changes in customer requirements are the main challenges faced by enterprises. Service-oriented architecture is considered as a practical solution to solve this problem for service-oriented enterprises. In the service-oriented architecture, selecti More

Today, the continuous changes in customer requirements are the main challenges faced by enterprises. Service-oriented architecture is considered as a practical solution to solve this problem for service-oriented enterprises. In the service-oriented architecture, selection and composition of services to quickly respond to complex customer requirements is available to service-oriented enterprises. Enterprises use ready-to-use and outsourced services to respond more quickly to the complex and changing needs of customers. One of the emerging technologies in this area is web services. By expanding the desire of enterprises to use web services, overtime web services providers increased. For this reason, Web services with the same functionality and different qualities were expanded. Therefore, the issue of choosing a web service with the best quality for enterprises is important. On the other hand, enterprises with only one web service cannot meet the complex requirements of customers; therefore, they need to composite multiple web services together. In addition, with the increase of web services with different functions, correlation, dependency and conflict between Web services also expand in their composition. But so far, there is no way to choose the best web services based on the quality of service(QoS) and also their composition does not violate the dependency, conflict and correlation between web services. In this paper, we try to make use of previous methods that consider dependency or conflict or correlation in simple modes of web services composition. We will improve all these methods in a comprehensive approach and support complex situations that may arise from the composition of web services and find the suitable composite web service by considering dependency, conflict, and correlation between Web services. Manuscript profile

Open Access Article

42 - Using Combined Classifier Based on the Separation of Conventional and Unconventional Samples to Diagnose Breast Cancer
amin rezaeipanah hesam vaghebin

Breast cancer is one of the most common types of cancers in women and in recent years there has been a significant increase in the number of people with this disease. With the increasing spread of science, data mining has become one of the most widely used areas for imp More

Breast cancer is one of the most common types of cancers in women and in recent years there has been a significant increase in the number of people with this disease. With the increasing spread of science, data mining has become one of the most widely used areas for improving therapeutic systems. In this paper, the diagnosis of breast cancer is performed in two steps. In the first step, an improved genetic algorithm is used to identify the desirable features in the prediction of this disease, and in the second stage, conventional and Unconventional samples are identified to increase the accuracy and create the final classification model. For classification work, a comparison between two decision tree and Support vector machine model is used to show the results of the superiority of the Support vector machine model. The results of the experiments reported the accuracy of breast cancer diagnosis on WBCD, WDBC and WPBC data sets are 99.26%, 98.55% and 98.45%, respectively. Manuscript profile

Open Access Article

43 - Coordinated Design of Power System Stabilizer and Variable Impedance Devices to Increase Damping of Inter-Area Modes Using Genetic Algorithm
m. zamani G. Shahgholian

Power system stabilizer (PSS) does not have a significant impact on inter-area modes and FACTS devices are used to damping these modes and to enhance power system stability. In this article, an objective function based on different and variable weight coefficients accor More

Power system stabilizer (PSS) does not have a significant impact on inter-area modes and FACTS devices are used to damping these modes and to enhance power system stability. In this article, an objective function based on different and variable weight coefficients according to eigenvalues condition is proposed and optimization parameters of power system stabilizer and variable impedance parameters include static VAR compensator (SVC) and thyristor controlled series capacitor (TCSC), (Including amplifying gain rate and time constant of phase-compensating blocks) is done using genetic algorithm in harmony. Also, in the process of optimization, the location of the FACTS devices and the control signal are considered as optimization parameters. Simulation results on IEEE 68-bus system show improvement damping of inter-area modes using the proposed method. Manuscript profile

Open Access Article

44 - A Semi-Central Method to Improve Energy Saving in Real Wireless Sensor Networks Using Clustering and Mobile Sinks
Fatemeh Sadeghi Sepideh Adabi Sahar Adabi

Applying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to criti More

Applying a hierarchical routing approach based on clustering technique and mobile sink has a great impact on reducing energy consumption in WSN. Two important issues in designing such an approach are cluster head selection and optimal allocation of mobile sinks to critical regions (i.e., regions those have low remaining energy and thus, high risk of energy hole problem). The limited number of mobile sinks should be utilized due to a high cost. Therefore, allocating the limited number of mobile sinks to the high amount of requests received from the critical regions is categorized as a NP-hard problem. Most of the previous studies address this problem by using heuristic methods which are carried out by sensor nodes. However, this type of solutions cannot be implemented in real WSN due to the sensors’ current technology and their limited processing capability. In other words, these are just theoretical solutions. Consequently, a semi-central genetic algorithm based method using mobile sink and clustering technique is proposed in order to find a trade-off between reduction of computation load on the sensors and increasing accuracy. In our method, lightweight computations are separated from heavyweight computations. While, the former computations are carried out by sensors, the latter are carried out by base station. Following activities are done by the authors: 1) cluster head selection by using effective environmental parameters and defining cost function of cluster membership, 2) mathematical modeling of a region’s chance to achieve mobile sink, and 3) designing a fitness function to evaluate the fitness of each allocation of mobile sinks to the critical regions in genetic algorithm. Furthermore, in our activities minimizing the number and length of messages are focused. In summary, the main distinguishing feature of the proposed method is that it can be implemented in real WSN (due to separation of lightweight computations from heavyweight computations) with respect to early mentioned objectives. The simulation results show the better performance of the proposed method compared to comparison bases. Manuscript profile

Open Access Article

45 - Increasing Image Quality in Image Steganography Using Genetic Algorithm and Reversible Mapping
Saeed TorabiTorbati مرتضی خادمی عباس ابراهیمی مقدم

One of the evaluation methods for image steganography is preserving cover image quality and algorithm imperceptibility. Placing hidden information should be done in such a way that there is minimal change in quality between the cover image and the coded image (stego ima More

One of the evaluation methods for image steganography is preserving cover image quality and algorithm imperceptibility. Placing hidden information should be done in such a way that there is minimal change in quality between the cover image and the coded image (stego image). The quality of the stego image is mainly influenced by the replacement method and the amount of hidden information or the replacement capacity. This can be treated as an optimization problem and a quality function can be considered for optimization. The variables of this function are the mappings applied to the cover image and the hidden information and location of the information. In the proposed method, by genetic algorithm and using the two concepts of targeted search and aimless search, the appropriate location and state for placement in the least significant bits of the cover image are identified. In this method, hidden information can be extracted completely and without error. This feature is important for management systems and cloud networks that use steganography to store information. Finally, the proposed method is tested and the results are compared with other methods in this field. The proposed method, in addition to maintaining the stego image quality, which is optimized based on PSNR, has also shown good performance in examining histogram and NIQE statistical criteria. Manuscript profile

Open Access Article

46 - Optimum orientation of the building with the aim of optimal shading and reducing energy consumption (Case Study: Tehran Music Hall)
Tiam Aram Javad Eiraji

The increasing trend of population growth, the energy crisis, and the depletion of energy resources on the planet are all warnings for all sciences and in all fields and professions in order to help sustain the existing situation. Since a large amount of energy consumpt More

The increasing trend of population growth, the energy crisis, and the depletion of energy resources on the planet are all warnings for all sciences and in all fields and professions in order to help sustain the existing situation. Since a large amount of energy consumption in the world is spent on construction purposes, specifically on cooling and heating loads and creating thermal comfort in the building, a study in this field is significantly important. In this research, by choosing a building as a case study, the amount of sunlight received by vertical surfaces has been investigated. Then, using the simulation method and related software, different angles between zero and 180 degrees of rotation are considered for the building to optimize the orientation angle of the building. The optimal angle means that the minimum amount of solar energy is received on vertical surfaces and the maximum amount of shading. Numerous research has been conducted in the past years about the amount of sunlight received in the building and the optimal angle. However, the used software and the measurement on vertical surfaces in Tehran in this research are considered research innovations. The optimal angle results from building energy analysis charts are displayed in this research. Manuscript profile

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