A decomposition based memetic multi-objective algorithm for continuous multi-objective optimization problem

Multi-objective evolution algorithm based on decomposition (MOEA/D) had been successfully applied into many multi-objective optimization problems, which had gained a lot of attention from the community of evolutionary algorithm(EA) in the past few years. In MOEA/D, a multi-objective optimization problem would be converted into a set of scalar single-objective subproblems and then utilize EA to address these subproblems simultaneously. In order to further improve its performance, a local search operator, which is designed via the diverse information of neighboring individuals in the search space, and a resource allocation strategy, which is used to balance the trade-off between genetic operator and local search operator, are both introduced into the framework of MOEA/D. A set of experiments are carried out to investigate the strength and weakness of our proposed algorithm on a series of benchmark test problems in comparison with the original MOEA/D.