A constrained multi-objective evolutionary algorithm based on dynamic clustering strategy

The dual-population co-evolution strategy is a class of methods that can efficiently solve constrained multi-objective optimisation problems. However, the auxiliary population does not contribute effective individuals to the main population at all stages of population evolution. Considering the utilisation of auxiliary population at later evolutionary stage, a constrained multi-objective evolutionary algorithm based on the dynamic clustering co-evolutionary strategy is proposed. This paper proposes a dynamic clustering strategy that dynamically divides the population into active and inactive populations based on the auxiliary population status, where only the active population participates in generating the offspring, so as to reasonably allocate the computational resources and enhance the convergence of the population. In addition, the feasible solutions found by the auxiliary population are retained using an additional archived population to improve the diversity of the main population. Experimental results demonstrate the effectiveness of the algorithm.