Roulette Electromagnetic Field Optimization (R-EFO) Algorithm

Abstract

Two key elements that influence the local search performance of meta-heuristic optimization algorithms are selection methods and search operators. In this article, the effect of roulette wheel, which is a probabilistic selection method, on local search performance of EFO (electromagnetic field optimization) algorithm, which is a current meta-heuristic search technique, is researched. In the EFO, the group of solution candidates are divided into positive, neutral and negative fields depending on their fitness values. The solution candidates selected from these three fields guide the search process. In this process, solution candidates are determined by greedy and random selection methods. In this study, the roulette technique is used for selection of solution candidates from negative field. In the experimental studies, the continuous valued and unconstrained problems CEC17 benchmark suite are used to test the performance of proposed Roulette- Electromagnetic Field Optimization (R-EFO). The results of the experimental study are statistically analyzed by Wilcoxon runk sum test used in comparison with standard EFO algorithm. According to the analysis results, proposed R-EFO algorithm with roulette selection method significantly improves the search performance of the EFO algorithm.

Keywords

• Electromagnetic field optimization,Roulette selection,CEC17 benchmark suite,Meta-heuristic optimization

Rulet Elektromanyetik Alan Optimizasyon (R-EFO) Algoritması

Abstract

Meta-sezgisel optimizasyon algoritmalarının yerel arama performansları üzerinde etkili olan iki temel öğe seçim yöntemleri ve arama operatörleridir. Bu makale çalışmasında olasılıksal bir seçim yöntemi olan rulet tekerleğinin güncel bir meta-sezgisel arama tekniği olan elektromanyetik alan optimizasyon (electromagnetic field optimization, EFO) algoritmasının yerel arama performansı üzerindeki etkisi araştırılmaktadır. Elektromanyetik optimizasyon algoritmasında çözüm adayları topluluğu uygunluk değerlerine bağlı olarak pozitif, nötr ve negatif alanlara ayrılmaktadır. Bu üç alandan seçilen çözüm adayları ise arama sürecine rehberlik etmektedirler. Bu süreçte çözüm adayları açgözlü ve rastgele seçim yöntemleri ile belirlenmektedir. Bu makale çalışmasında ise negatif alandan çözüm adaylarının seçimi için rulet tekniği kullanılmaktadır. Deneysel çalışmalarda literatürdeki en güncel sürekli değer problemleri olan CEC17 test seti kullanılmıştır. Deneysel çalışma sonuçları istatistiksel olarak ikili karşılaştırmalarda kullanılan wilcoxon runk sum test ile analiz edilmiştir. Analiz sonuçlarına göre rulet seçim yöntemi EFO algoritmasının arama performansını kayda değer şekilde artırmaktadır.

Keywords

• Elektromanyetik alan optimizasyon algoritması,rulet seçim yöntemi,CEC17 karşılaştırma problemleri,meta-sezgisel optimizasyon

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