A Novel Hybrid Algorithm Based on Bacterial Foraging Optimization and Grey Wolf Optimizer

Autor:	Baoyu Xiao, Xiaobing Gan
Rok vydání:	2020
Předmět:	Mathematical optimization Fitness proportionate selection Function optimization Computer science Foraging Mutation (genetic algorithm) Benchmark (computing) Swarm behaviour Bacterial population Hybrid algorithm
Zdroj:	Intelligent Computing Theories and Application ISBN: 9783030608019 ICIC (2)
DOI:	10.1007/978-3-030-60802-6_40
Popis:	A novel hybrid algorithm named GMBFO, with the combination between Grey Wolf Optimizer and the modified Bacterial Foraging Optimization, is presented in the paper. To improve the fixed chemotaxis step size in the standard BFO algorithm, the paper incorporates a nonlinear-decreasing adaptive mechanism into BFO. Besides that, an effective swarm learning strategy with the other three current global best individuals is proposed. In the dispersal and elimination step, we adopt the roulette wheel selection and local mutation mechanism to improve the diversity of the whole bacterial population. To testify the optimization performance of the proposed GMBFO, six benchmark functions with 45 dimensions are selected. Compared with BFO and the other three BFO variants, the GMBFO algorithm has an excellent capability in function optimization.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f0919d5b43f617b2fc4fd05a74ed0558 https://doi.org/10.1007/978-3-030-60802-6_40 Zobrazit plný text záznamu