A powerful variant of symbiotic organisms search algorithm for global optimization
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Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This paper suggests a new variation to the existing symbiotic organisms search (SOS) algorithm developed by simulating three symbiotic strategies of mutualism, commensalism and parasitism used by the organisms. In the revised version called improved SOS (ISOS), the theory of quasi-oppositional based learning is employed during generation of initial population and in the parasitism phase to raise the possibility of getting closer to high-quality solutions. An efficient alternative for parasitism phase is also presented. The two upgraded parasitism strategies avoid the over exploration issue of original parasitism phase that causes unwanted longtime search in the inferior search space as the solution is already refined. To guide the algorithm perform an exhaustive search around the best solution in attempting to further improve the search model of ISOS, a chaotic local search based on the piecewise linear chaotic map is coupled into the proposed algorithm. Twentysix benchmark functions and three engineering design problems are tested and a contrast with other popular metaheuristics is widely established. Comparative results substantiate the great contribution of proposed ISOS algorithm in solving various optimization problems with superior global search capability and convergence characteristics which render it useful in handling global optimization problems.
Açıklama
Anahtar Kelimeler
Symbiotic organisms search, Quasi-oppositional based learning, Chaotic theory, Local search, Benchmark function, Engineering design, Global optimization, Automatic Voltage Regulator, Hybrid Genetic Algorithm, Pid Controller, Performance Analysis, Efficient Design, Opposition
Kaynak
Engineering Applications Of Artificial Intelligence
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
87
