Magnetotactic bacteria optimisation algorithm with self-regulation interaction energy. (25th November 2021)

• Record Type: Journal Article
• Authors: Zhao, Jiao; Mo, Hongwei
• Is Part Of: International journal of bio-inspired computation. Volume 18:Number 3(2021)
• Page Start: 189
• Page End: 198
• Publication Date: 2021-11-25
• Subjects: magnetotactic bacteria optimisation algorithm -- MBOA -- self-regulation interaction energy -- exploration -- global convergence; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

Magnetotactic bacteria optimisation algorithm (MBOA) is a new optimisation algorithm inspired by the biology characteristics of magnetotactic bacteria in the nature. The original MBOA is vulnerable to premature convergence and has poor exploration capability. In this paper, a modified magnetotactic bacteria optimisation algorithm (MMBOA) is proposed to improve the performance of algorithm. It uses a kind of self-regulation interaction energy to enhance the diversity of the swarm for encouraging broader exploration. And as the number of iterations increases, the self-regulation interaction energy can keep MMBOA convergence. Convergence analysis of MMBOA is also implemented. The proposed algorithm converges to the global optimum with a probability one when the number of iterations tends to infinity. Experimental results on CEC2013 and a part of CEC2011 benchmark functions show that the proposed algorithm is efficient and robust, especially in optimising multimodal functions.