Title: Magnetotactic bacteria optimisation algorithm with self-regulation interaction energy
Authors: Jiao Zhao; Hongwei Mo
Addresses: Automation College, Harbin Engineering University, Harbin, China ' Automation College, Harbin Engineering University, Harbin, China
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.
Keywords: magnetotactic bacteria optimisation algorithm; MBOA; self-regulation interaction energy; exploration; global convergence.
DOI: 10.1504/IJBIC.2021.119195
International Journal of Bio-Inspired Computation, 2021 Vol.18 No.3, pp.189 - 198
Accepted: 21 May 2020
Published online: 29 Nov 2021 *