Bird mating optimizer: An optimization algorithm inspired by bird mating strategies

作者: Alireza Askarzadeh

DOI: 10.1016/J.CNSNS.2013.08.027

关键词: MatingBenchmark (computing)Genetic algorithmContinuous optimizationArtificial intelligenceComputer scienceOptimization problemEvolutionary algorithmOptimization algorithm

摘要: Abstract Thanks to their simplicity and flexibility, evolutionary algorithms (EAs) have attracted significant attention tackle complex optimization problems. The underlying idea behind all EAs is the same they differ only in technical details. In this paper, we propose a novel version of EAs, bird mating optimizer (BMO), for continuous problems which inspired by strategies species during season. BMO imitates behavior metaphorically breed broods with superior genes designing optimum searching techniques. On large set unimodal multimodal benchmark functions, represents competitive performance other EAs.

参考文章(24)
David H. Wolpert, William G. Macready, No Free Lunch Theorems for Search Research Papers in Economics. ,(1995)
Agoston E. Eiben, J. E. Smith, Introduction to evolutionary computing ,(2003)
Yan-jun Shi, Hong-fei Teng, Zi-qiang Li, Cooperative Co-evolutionary Differential Evolution for Function Optimization Lecture Notes in Computer Science. pp. 1080- 1088 ,(2005) , 10.1007/11539117_147
Xin Yao, Yong Liu, Fast Evolution Strategies Evolutionary Programming. ,vol. 26, pp. 151- 162 ,(1997) , 10.1007/BFB0014808
Alireza Askarzadeh, Alireza Rezazadeh, A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer International Journal of Energy Research. ,vol. 37, pp. 1196- 1204 ,(2013) , 10.1002/ER.2915
A. I.F. Vaz, L. N. Vicente, PSwarm: a hybrid solver for linearly constrained global derivative-free optimization Optimization Methods & Software. ,vol. 24, pp. 669- 685 ,(2009) , 10.1080/10556780902909948