Crow Search Algorithm with Incremental Population

Abstract

The crow search algorithm (CSA) is one of the new metaheuristics based on the intelligence behavior of crows. Although its simple structure and the need for few parametric adjustments give it an advantage, the problem of early convergence and easily falling to the local optimum decreases its performance in multimodal problems. In this paper, incremental population (IPOP) based CSA (IPOP-CSA) algorithm has been developed to strengthen this weakness of CSA and increase its efficiency. Using the IPOP strategy based on an expanding population, it is aimed to maintain the diversity of solutions throughout evolution. The four CSA versions developed are applied to 100-dimensional test functions to monitor their performance. The results obtained show that the proposed methods improve the performance of the basic CSA.

Keywords

• Metaheuristic
• Optimization
• Crow search algorithm

Artımsal Popülasyonlu Karga Arama Algoritması

Abstract

Karga arama algoritması (CSA) kargaların zekâ davranışlarına temellendirilen yeni meta-sezgisellerden biridir. Basit yapısı ve az sayıda parametreye ihtiyaç duyuşu ona avantaj sağlamasına rağmen, erken yakınsama problemi ve yerel optimuma kolayca düşmesi özellikle çokmodlu (MM) problem çözümlerinde performansını düşürmektedir. Bu çalışmada, CSA ‘nın bu zayıflığını güçlendirmek ve etkinliğini arttırmak için artımsal popülasyon (IPOP) temelli CSA (ICSA) algoritmaları geliştirilmektedir. Genişleyen bir popülasyonu temel alan IPOP stratejisi ile hesaplama boyunca çözüm çeşitliliğin sağlanması hedeflenmektedir. Geliştirilen dört adet CSA versiyonu 100-boyutlu test fonksiyonlarına uygulanarak performansları gözlemlenmiştir. Elde edilen sonuçlar, önerilen metotların temel CSA ’nın performansını iyileştirdiğini göstermektedir.

Keywords

• Metazesgisel
• Optimizasyon
• Karga arama algoritması

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