Magnetotactic bacteria optimisation algorithm with self-regulation interaction energy Online publication date: Mon, 29-Nov-2021
by Jiao Zhao; Hongwei Mo
International Journal of Bio-Inspired Computation (IJBIC), Vol. 18, No. 3, 2021
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.
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