PERFORMANCE COMPARISON OF THE SPECIALIZED ALPHA MALE GENETIC ALGORITHM WITH SOME EVOLUTIONARY ALGORITHMS

Year 2019, Volume 21, Issue 1, 55 - 82, 30.06.2019

Mehmet Hakan SATMAN Emre AKADAL

https://doi.org/10.26468/trakyasobed.452095

Abstract

Alpha Male Genetic Algorithms are sexist and population based optimization tools that mimic the swarm behavior of animals. The algorithm consists on a socially partitioned population of individuals where the partitions are formed by sexual selection of females. In this paper, we suggest to use Linear Crossover and Hooke-Jeeves method for crossover and hybridization operators of Alpha Male Genetic Algorithms, respectively. We perform a simulation study using a set of well-known test functions to reveal performance differences between the specialized algorithm and some other well-known optimization techniques including Genetic Algorithms, Differential Evolution, Particle Swarm Optimization, and Artificial Bee Colony Optimization. Simulation results show that the specialized algorithm outperforms its counterparts in most of the cases.

Keywords

Optimization, Evolutionary algorithms, Simulations

ÖZELLEŞTİRİLMİŞ ALFA ERKEK (ALPHA MALE) GENETİK ALGORİTMANIN EVRİMSEL ALGORİTMALARLA PERFORMANS KARŞILAŞTIRMASI

Abstract

Alfa erkek genetik algoritmalar cinsiyet farkı gözeten ve hayvan gruplarının hareketlerini taklit eden topluluk tabanlı bir optimizasyon aracıdır. Algoritma, dişilerin eş seçimi ile oluşturduğu sosyal olarak bölünmüş birey topluluklarına dayanmaktadır. Çalışmada, Alfa Erkek Genetik Algoritma’nın çaprazlama ve hibritleşme operatörü olarak sırasıyla Doğrusal Çaprazlama ve Hooke-Jeeves yöntemi kullanılması önerilmiştir. Çalışma kapsamında özelleştirilmiş algoritma ile Genetik Algoritmalar, Diferansiyel Evrim, Parçacık Sürü Optimizasyonu ve Yapay Arı Kolonisi Optimizasyonu gibi iyi bilinen algoritmalar arasındaki performans farklılıklarını ortaya çıkarabilmek için bilinen test fonksiyonları ile bir simülasyon çalışması gerçekleştirilmiştir. Simülasyon sonuçları, özelleştirilmiş algoritmanın çoğu durumda daha iyi performans sergilediğini göstermiştir.

Keywords

Optimizasyon, Evrimsel algoritmalar, Simülasyon

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