Magnetotactic Bacteria Constrained Optimization Algorithm
Many problems encountered in the field of science and engineering can be ultimately attributed to constrained optimization problems (COPs). During the past decades, solving COPs with evolutionary algorithms have received considerable attentions among researchers. A novel approach to deal with numerical constrained optimization problems, which incorporates a Magnetotactic Bacteria Optimization Algorithm (MBOA) and an adaptive constraint-handling technique, named COMBOA, is presented in this paper. COMBOA mainly consists of an improved MBOA and archiving-based adaptive tradeoff model (ArATM) used as the constraint-handling technique. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. It is compared with several state-of-the-art algorithms on 13 well-known benchmark functions. The experiment results show that the COMBOA is effective in solving constrained optimization problems. It shows better or competitive performance compared with other state-of-the-art algorithms referred to in this paper in terms of the quality of the solutions.
KeywordsConstrained optimization Magnetic bacteria optimization algorithm Constraint-handling mechanism Adaptive tradeoff model Individual archiving technique
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