Abstract
This paper presents a multi-objective cellular memetic algorithm (MOCMA) based on k-means clustering, which integrates the clustering-based local search method into multi-objective cellular genetic algorithm. Specifically, according to the objective function values of individuals in each generation, the k-means clustering is used to control the similar individuals gathered in a cluster. Meanwhile, to explore the search space efficiently and get the Pareto optimal solutions in objective space, one individual is selected randomly to undergo local search from each cluster and it will be improved than before. The MOCMA is applied to constrained and unconstrained problems. We analyse the influence of cluster number on the performance of the algorithm, and compare the MOCMA with other evolutionary multi-objective optimisers. It indicates that the proposed MOCMA is efficient for solving the multi-objective optimisation problems.
Published Version
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