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

Yıl 2019, , 55 - 82, 30.06.2019

Mehmet Hakan Satman , Emre Akadal

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

Öz

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.

Anahtar Kelimeler

Optimization, Evolutionary algorithms, Simulations

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

Öz

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.

Anahtar Kelimeler

Optimizasyon, Evrimsel algoritmalar, Simülasyon

Kaynakça

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