Fuzzy multiobjective differential evolution using performance metrics feedback

Differential evolution is regarded as one of the most efficient evolutionary algorithms to tackle multiobjective optimization problems. The key to success of any multiobjective evolutionary algorithms (MOEAs) is maintaining a delicate balance between exploration and exploitation throughout the evolution process. In this paper, we propose a Fuzzy-based Multiobjective Differential Evolution (FMDE) that uses performance metrics, specifically, hypervolume, spacing, and maximum spread, to measure the state of the evolution process. We apply the inference rules to these metrics in order to dynamically adjust the associated control parameters of a chosen mutation strategy used in this algorithm. One parameter controls the degree of greedy or exploitation, while another regulates the degree of diversity or exploration of the reproduction phase. Therefore, we can appropriately adjust the degree of exploration and exploitation through performance feedback. The performance of FMDE is evaluated on well-known ZDT and DTLZ test suites in addition two representative functions in WFG. The results show that the proposed algorithm is competitive with respect to chosen state-of-the-art MOEAs.