A two-population constrained multi-objective optimization algorithm based on a dynamic ε constraint processing mechanism
Constrained multi-objective optimization problems(CMOPs)need to meet certain constraints in addition to solving multiple conflicting objectives.In view of the fact that the existence of constraints causes the Pareto front of CMOPs to be di-vided into multiple parts,while the expansion of infeasible regions further hinders the exploration of the population,causing the population to fall into a local optimum and its diversity to decrease dramatically,this paper proposes a dual-population-based optimization algorithm for constrained multi-objective optimization based on dynamic epsilon constraints processing mechanism.The algorithm uses a dual-population co-evolutionary strategy,in which the main population takes constraints in-to consideration and makes full use of the effective information provided by infeasible solutions through the improved dynam-ic epsilon constraint processing mechanism,while the auxiliary population does not consider constraints and converges rapid-ly to the Unconstrained Pareto Front(UPF)based on balancing the diversity,and provides effective information outside the feasible domain to the main population in a timely manner,and also provide the main population with effective information outside the feasible domain in time to guide the exploration direction of the main population.The experimental results show that the proposed algorithm is more competitive than other algorithms in the MW testing problems.
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