Elymus Repens Optimization (ERO); A Novel Agricultural-Inspired Algorithm
Subject Areas : IT Strategy
1 -
Keywords: Elymus Repens Optimization, meta-heuristic algorithms, Rhizome Optimization Operator, Stolon Optimization Operator,
Abstract :
Optimization plays a crucial role in enhancing productivity within the industry. Employing this technique can lead to a reduction in system costs. There exist various efficient methods for optimization, each with its own set of advantages and disadvantages. Meanwhile, meta-heuristic algorithms offer a viable solution for achieving the optimal working point. These algorithms draw inspiration from nature, physical relationships, and other sources. The distinguishing factors between these methods lie in the accuracy of the final optimal solution and the speed of algorithm execution. The superior algorithm provides both precise and rapid optimal solutions. This paper introduces a novel agricultural-inspired algorithm named Elymus Repens Optimization (ERO). This optimization algorithm operates based on the behavioral patterns of Elymus Repens under cultivation conditions. Elymus repens is inclined to move to areas with more suitable conditions. In ERO, exploration and exploitation are carried out through Rhizome Optimization Operator and Stolon Optimization Operators. These two supplementary activities are used to explore the problem space. The potent combination of these operators, as presented in this paper, resolves the challenges encountered in previous research related to speed and accuracy in optimization issues. After the introduction and simulation of ERO, it is compared with popular search algorithms such as Gravitational Search Algorithm (GSA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The solution of 23 benchmark functions demonstrates that the proposed algorithm is highly efficient in terms of accuracy and speed.
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[45]. B. Wang, X. Jin, B. Cheng, "Lion pride optimizer: An optimization algorithm inspired by lion pride behavior", Sci. China Inf. Sci., Vol. 55, No. 10, pp. 2369–2389, 2012.
[46]. A.H. Gandomi, A.H. Alavi, "Krill herd: A new bio-inspired optimization algorithm", Commun. Nonlinear Sci., Vol. 17 , No. 12, pp. 4831–4845, 2012.
[47]. S. Mirjalili, S.M. Mirjalili, A. Lewis, "Grey wolf optimizer", Adv. Eng. Softw., Vol. 69 , pp. 46–61, 2014.
[48]. A.H. Gandomi, X.S. Yang, A.H. Alavi, "Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems", Eng. Comput., Vol. 29, No. 1, pp. 17–35, 2013.
[49]. S. Mirjalili, "Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems", Neural Comput. Appl., Vol. 27, No. 4 , pp. 1053–1073, 2016.
[50]. S. Mirjalili, "A. Lewis, The whale optimization algorithm", Adv. Eng. Softw., Vol. 95, pp. 51–67, 2016.
[51]. S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, S.M. Mirjalili, "Salp swarm algorithm: A bio-inspired optimizer for engineering design problems", Adv. Eng. Softw., Vol. 114, pp.163–191, 2017.
[52]. A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, H. Chen, "Harris hawks optimization: Algorithm and applications", Future Gener. Comput. Syst., Vol. 97 pp. 849–872, 2019.
[53]. G. Azizyan, F. Miarnaeimi, M. Rashki, N. Shabakhty, "Flying squirrel optimizer (FSO): A novel SI-based optimization algorithm for engineering problems", Iran. J. Optim., Vol. 11, No. 2, pp.177–205, 2019.
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