Burhanettin DURMUŞ, Hasan TEMURTAŞ, Ayhan GÜN

Artımsal Popülasyonlu Karga Arama Algoritması

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

Crow Search Algorithm with Incremental Population

The crow search algorithm (CSA) is one of the new metaheuristics based on the intelligence behavior of crows. Although its simplestructure and the need for few parametric adjustments give it an advantage, the problem of early convergence and easily falling to thelocal 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 onan expanding population, it is aimed to maintain the diversity of solutions throughout evolution. The four CSA versions developed areapplied to 100-dimensional test functions to monitor their performance. The results obtained show that the proposed methods improvethe performance of the basic CSA.

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