In this paper we present effect of the chaotic crossover operator with different chaotic maps on the metaheuristic search algorithm Breeding Swarms algorithm which is the Particle Swarm Optimization’s one of the genetic algorithm hybrid form. Some of the many optimization problems could have too many local extrema. Most of the time optimization algorithms could stuck on these extrema therefore these algorithms could have trouble with finding global extremum. To avoiding local extrema and conduct better search on search space, a chaotic number generator is used on Breeding Swarms algorithm’s most of the random procedures. To test efficiency and randomness of the chaotic crossover operator, different chaotic maps are used on the Breeding Swarm algorithm. Test and performance evaluations are conducted on Multimodal and unimodal benchmark functions. This new approach showed us that modified Breeding Swarms algorithm yielded slightly better results than Particle Swarm Optimization and original Breeding Swarms algorithms on tested benchmark functions.
|Konular||Bilgisayar Bilimleri, Yapay Zeka|
|Yayımlanma Tarihi||30 Nisan 2021|
|Başvuru Tarihi||18 Eylül 2020|
|Kabul Tarihi||12 Mart 2021|
|Yayınlandığı Sayı||Yıl 2021, Cilt 4, Sayı 1|