K-Ortalamalara dayalı yeni yoğunlaştırma stratejisi ile Parçacık Sürüsü Optimizasyonu

Abstract

Parçacık Sürü Optimizasyonu (PSO), sürü zekâsı temelli metaheuristik algoritmadır. PSO, balıkların veya kuşların yiyecek arama davranışlarından esinlenilerek modellenmiştir. PSO, sade ve etkili bir çalışma yapısına sahip olmasının avantajlarına rağmen, erken yakınsama, yerel minimuma takılma ve zayıf küresel arama kapasitesi gibi bazı dezavantajları da bulunmaktadır. Bu çalışmada, PSO'nun performansını artırmak için K-Ortalamalar kümelemeye dayalı yeni bir yoğunlaştırma stratejisi önerilmiştir. Önerilen yönteme, K-Ortalamalara Dayalı Yeni Yoğunlaştırma Stratejisi ile Parçacık Sürüsü Optimizasyonu (PSO-ISK) adı verilmiştir. PSO-ISK'nın ilk adımında, PSO'daki parçacıklar farklı kümelere ayrılmaktadır. Sonraki adımda ise, her küme için bir merkez ve merkeze en uzak parçacık belirlenmektedir. Bu çalışmanın sonucunda, PSO-ISK, merkeze en uzak parçacığın sonuçlarını iyileştirerek yeni bir yoğunlaştırma stratejisi önermektedir. PSO-ISK'nın performansı, 16 farklı benchmark test fonksiyonu kullanılarak sonuçlar analiz edilmiştir. Elde edilen sonuçlar, Standart PSO (SPSO) ve 7 farklı PSO varyantı ile karşılaştırılmıştır. Yapılan karşılaştırmalar sonucunda, PSO-ISK'nın diğer çalışmalara göre daha etkili sonuçlar elde ettiği ve PSO-ISK'nın performans iyileştirmesindeki önemi kanıtlamıştır.

Keywords

• K-Ortalamalar
• PSO
• Yoğunlaştırma stratejisi

Particle Swarm Optimization with a new intensification strategy based on K-Means

Abstract

Particle Swarm Optimization (PSO) is a swarm intelligence-based metaheuristic algorithm inspired by the foraging behaviors of fish or birds. Despite the advantages of having a simple and effective working structure, PSO also has some disadvantages, such as early convergence, getting trapped in local minima, and weak global search capabilities. In this study, a novel intensification strategy based on K-Means clustering has been proposed to enhance the performance of PSO. The proposed method is called Particle Swarm Optimization with a New Intensification Strategy based on K-Means (PSO-ISK). In the first step of PSO-ISK, particles in PSO are grouped into different clusters. Then, a center and the farthest particle from the center are identified for each cluster. PSO-ISK proposes a new intensification strategy by improving the results of the farthest particle from the center. The performance of PSO-ISK is analyzed using 16 different benchmark test functions. The obtained results are compared with Standard PSO (SPSO) and 7 different PSO variants. According to the comparison results, PSO-ISK provides a notable performance improvement by outperforming SPSO and all seven PSO variants. The comparisons conducted have proven that PSO-ISK produces more effective outcomes than other studies, which results in a significant contribution to improving performance.

Keywords

• Intensification strategy
• K-Means
• PSO

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