A Differential Mutation operator for the archive population of multi-objective evolutionary algorithms

The Differential Evolution (DE) algorithm is a simple and efficient evolutionary algorithm that has been applied to solve many optimization problems mainly in continuous search domains. In the last few years, many implementations of multi-objective versions of DE have been proposed in the literature, combining the traditional differential mutation operator as the variation mechanism and some form of Pareto-ranking based fitness. In this paper, we propose the utilization of the differential mutation operator as an additional operator to be used within any multi-objective evolutionary algorithm that employs an archive (offline) population. The operator is applied for improving the high-quality solutions stored in the archive, working both as a local search operator and a diversity operator depending on the points selected to build the differential mutation. In order to illustrate the use of the operator, it is coupled with the NSGA-II and the multi-objective DE (MODE), showing promising results.