یک الگوریتم فراابتکاری پیوسته جدید و گسسته سازی آن جهت بیشینه سازی نفوذ در شبکه های پیچیده
محورهای موضوعی : فناوری اطلاعات و ارتباطات
وحیده سحرگاهی
1
,
وحید مجیدنژاد
2
,
Saeed Taghavi Afshord
3
,
باقر جعفری
4
1 - ّدانشجو
2 - هیات علمی
3 - Computer Engineering
4 - هیات علمی
کلید واژه: الگوریتم بهینهسازي علفهاي هرز, الگوریتمهاي جستجوي گرانشي, الگوریتم IWOGSA,
چکیده مقاله :
طبق نظریه ناهار مجاني (NFL) هیچ الگوریتم فرا اکتشافي موجود قادر به حل همه نوع مسائل به طور کارآمد نیست، بنابراین هر ساله الگوریتمهاي جدیدي جهت تنوع بخشي پیشنهاد ميشوند. در این مقاله، الگوریتم فراابتکاري جدیدي به نام IWOGSA ، براي مسائل بهینهسازي پیوسته پیشنهاد شده است که ترکیبي از الگوریتمهاي بهینهسازي علفهاي هرز و جستجوي گرانشي است. در IWOGSA والدها به دو صورت تکثیر مي شوند و از هر دسته نمونههایي براي انتقال به نسل جدید انتخاب ميگردد. بخشي از تکثیر با توزیع نرمال صورت ميگیرد و بخشي دیگر بر مبناي روابط سرعت و شتاب حرکت سیارات در الگوریتم جستجوي گرانشي انجام ميشوند. یک مدل گسسته جدید از IWOGSA به نام DIWOGSA براي حل مسألههاي بهینهسازي گسسته پیشنهاد شده است و کارایي آن بر روي یک چالش حیاتي تحت عنوان بیشینهسازي نفوذ ارزیابي شده است. در DIWOGSA از رویکرد هوشمندانهاي براي مقداردهي اولیه جمعیت استفاده شده و براي همگرایي سریعتر الگوریتم، یک عملگر جستجوي محلي پیشنهاد شده است. در حالت پیوسته الگوریتم IWOGSA با توابع بنچمارک استاندارد و کامپوزیت و 3 مساله مهندسي رایج ارزیابي شده است. نتایج پیادهسازي ثابت ميکند که الگوریتم IWOGSA در مقایسه با روشهاي اخیر و متداول بسیار رقابتي بوده و با توجه به نتایج رتبهبندي آزمون فریدمن، توانسته است رتبه اول را کسب نماید. در حالت گسسته نیز الگوریتم DIWOGSA با در نظر گرفتن شبکههاي مختلف ارتباطاتي بین محققان براي مساله بیشینهسازي نفوذ مورد ارزیابي قرار گرفته و در مقایسه با الگوریتمهاي رایج در این زمینه از نظر میزان نفوذ و زمان اجرا نتایج قابل قبولي را کسب کرده است.
In light of the No Free Lunch (NFL) theorem, which establishes the inherent limitations of meta-heuristic algorithms in universally efficient problem solving, the ongoing quest for enhanced diversity and efficiency prompts the introduction of novel algorithms each year. This research presents the IWOGSA meta-heuristic algorithm, a pioneering solution tailored for addressing continuous optimization challenges. IWOGSA ingeniously amalgamates principles from both the invasive weed optimization algorithm and the gravitational search algorithm, capitalizing on their synergies. The algorithm's key innovation lies in its dual-pronged sample generation strategy: a subset of samples follows a normal distribution, while others emulate the planetary motion-inspired velocities and accelerations from the gravitational search algorithm. Furthermore, a selective transfer of certain samples from distinct classes contributes to the evolution of succeeding generations. Expanding upon this foundation, a discrete variant of IWOGSA, termed DIWOGSA, emerges to tackle discrete optimization problems. The efficacy of DIWOGSA is demonstrated through its application to the intricate influence maximization problem. DIWOGSA distinguishes itself with an astute population initialization strategy and the integration of a local search operator to expedite convergence. Empirical validation encompasses a rigorous assessment of IWOGSA against established benchmark functions, composite functions, and real-world engineering structural design problems. Remarkably, the IWOGSA algorithm asserts its superiority, eclipsing both contemporary and traditional methods. This ascendancy is statistically affirmed through the utilization of the Friedman test rank, positioning IWOGSA as the premier choice. Also, DIWOGSA algorithm is evaluated by considering different networks for influence maximization problem, and it shows acceptable results in terms of influence and computational time in comparison to conventional algorithms.
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