A bare bones bacterial foraging optimization algorithm. (December 2018)

• Record Type: Journal Article
• Authors: Wang, Liying; Zhao, Weiguo; Tian, Yulong; Pan, Gangzhu
• Is Part Of: Cognitive systems research. Volume 52(2018)
• Page Start: 301
• Page End: 311
• Publication Date: 2018-12
• Subjects: Intelligent computing -- Global optimization -- Particle swarm optimization -- Metaheuristic -- Bare bones -- Chemotactic -- Reproduction; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.07.022
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: Bacterial foraging optimization (BFO), based on the social foraging behaviors of bacteria, is a new intelligent optimizer. It has been widely accepted as an optimization algorithm of current interest for a variety of fields. However, compared with other optimizers, the BFO possesses a poor convergence performance over complex optimization problems. To improve the optimization capability of the BFO, in this paper a bare bones bacterial foraging optimization (BBBFO) algorithm is developed. First, a chemotactic strategy based on Gaussian distribution is incorporated into this method through making use of both the historical information of individual and the share information of group. Then the swarm diversity is introduced in the reproduction strategy to promote the exploration ability of the algorithm. The performance of BBBFO is verified on various benchmark functions, the comparative results reveal that the proposed approach is more superior to its counterparts.