Fahri VATANSEVER, Yiğit Çağatay KUYU

Güncel Metasezgisel Algoritmalar İçin Kaos Tabanlı Yaklaşımlar

Hesaplama teknolojilerindeki hızlı gelişmelerle orantılı olarak, optimizasyon problemlerinin çözümünde evrimsel/sezgisel/metasezgisel algoritmalardan birçok alandaki uygulamalarda sıklıkla faydalanılmaktadır. Günümüzde, yeni algoritmalar geliştirilmekte ve mevcut algoritmalara yenilikler uygulanmaya devam edilmektedir. Bu çalışmada, son zamanlarda geliştirilmiş olan metasezgisel algoritmalardan olan: Geri İzleme Arama (BS), Gri Kurt Optimizasyon (GWO) ve Girdap Arama (VS) algoritmalarına kaos tabanlı modifikasyonlar önerilmiş ve algoritmaların, kıyaslamalarla detaylı analizleri gerçekleştirilmiştir. Önerilen yaklaşımlar, algoritmaların çözümlerini geliştirmek için işlemlerinde kullandıkları bazı rassal değişkenler yerine, kaos haritalarına dayanan yeni değişkenlerin üretilmesi temeline dayanmaktadır. Bunun yanında, kaos tabanlı bu değişkenler kullanılarak algoritmaların optimizasyon sürecinde kullandıkları yapısal işlemlerinde modifikasyonlar gerçekleştirilmektedir. Algoritmaların performansları; istatistiksel ve yakınsama hızları açısından, iki yönlü olarak analiz edilmektedir. Kaotik haritalara dayanan yaklaşımların, orijinal algoritmalar üzerinde daha iyi veya en azından karşılaştırılabilir sonuçlar ürettiği, gerçekleştirilen deneylerde gösterilmiştir.

THE CHAOS-BASED APPROACHES FOR ACTUAL METAHEURISTIC ALGORITHMS

Along with rapid developments in computational technologies,evolutionary/heuristic/metaheuristic algorithms have frequently used in many applications to solveoptimization problems. Nowadays, new algorithms are being developed and improvements are beingmade to existing algorithms. In this study, chaos-based modifications have been proposed for recentlydeveloped metaheuristic algorithms: Backtracking Search (BS), Grey Wolf Optimizer (GWO) and VortexSearch (VS), and the algorithms have been analyzed by detailed comparisons. The proposed approachesare based on generating new values through chaos maps, rather than some random numbers normallyused in the algorithms, to improve their solutions. In addition, some modifications are performed to thestructural operations of the algorithms used in the optimization process by taking advantage of chaosbasedvalues. The performances of the algorithms are evaluated by considering two metrics: convergencerates and statistical results. Experiments demonstrated that the performance of the algorithms with theproposed modifications based on the chaos approach, are better than, or at least comparable to, theoriginal algorithms.

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