Elephant Herding Optimization Using Multi-Search Strategy for Continuous Optimization Problems

Abstract

The elephant herding optimization (EHO), which imitates social behaviors of the elephants, is recently proposed a swarm intelligence and population-based optimization algorithm. Although EHO is a good at local search, it is not effective on the global search due to the rapid loss of population diversity. In the basic EHO method, a single solution search equation is used for the generating the new individuals. Therefore, it is insufficient on the solving the problems which have different characteristics and the exploring the search space effectively. In this study, in order to overcome these problems and to provide a balance between exploration and exploitation, elephant herding optimization using multi-search strategy (Multi-EHO) has been proposed which inspired by the search strategies of the most well-known optimization techniques. For the comparison of the proposed method and the basic EHO, the CEC2015 benchmark set with 15 different functions is used. In addition, to validate the performance of Multi-EHO, the proposed method is compared with the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA) proposed in recent years. Experimental results show that the proposed method has more successful and more robust performance than other methods.-

Keywords

• Elephant herding optimization,continuous optimization,search strategies,multi-search strategy

Sürekli Optimizasyon Problemleri için Çoklu Arama Stratejisi Kullanan Fil Sürü Optimizasyonu

Abstract

Fillerin sosyal davranışlarını taklit eden fil sürü optimizasyonu (EHO), yakın zamanda önerilen sürü zekası ve popülasyon tabanlı bir optimizasyon algoritmasıdır. EHO, yerel arama konusunda iyi bir yeteneğe sahip olmasına rağmen popülasyon çeşitliliğini erken kaybetmesi nedeniyle global aramada etkili olamamaktadır. Temel EHO yönteminde, yeni bireylerin oluşturulması için tek bir çözüm arama denklemi kullanılmaktadır. Bu nedenle, arama uzayının etkili bir şekilde araştırılmasında ve farklı karakteristikteki problemlerin çözümünde yetersiz kalmaktadır. Bu çalışmada, bu sorunların üstesinden gelmek ve keşif ve faydalanma arasındaki dengeyi sağlayabilmek için en çok bilinen optimizasyon tekniklerinin arama stratejilerinden esinlenilerek çoklu arama stratejisi kullanan fil sürü optimizasyonu (Multi-EHO) önerilmiştir. Önerilen yöntem ile temel EHO‘nun karşılaştırılması için farklı karakteristikteki 15 fonksiyona sahip CEC2015 test seti kullanılmıştır. Ayrıca Multi-EHO’nun performansını doğrulamak için, önerilen yöntem son yıllarda önerilen gri kurt algoritması (GWO) ve balina optimizasyonu algoritması (WOA) ile karşılaştırılmıştır. Deneysel sonuçlar, önerilen yöntemin diğer yöntemlere göre daha başarılı ve daha sağlam bir performansa sahip olduğunu göstermektedir.

Keywords

• Fil sürü optimizasyonu,sürekli optimizasyon,arama stratejileri,çoklu arama stratejisi

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