Multiobjective Differential Evolution Based on Fuzzy Performance Feedback

Differential evolution is often 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, the authors 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. The authors apply the fuzzy 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, the authors 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. The results validate that the proposed algorithm is competitive with respect to chosen state-of-the-art MOEAs.