A novel dynamic crowding distance based diversity maintenance strategy for MOEAs

Preserving population diversity is crucial for the performance of multi-objective evolutionary algorithms (MOEAs). In this paper, we propose a novel dynamic crowding distance based diversity preserving strategy for MOEAs. In the proposed strategy, the crowding distance is calculated based on the degree of deviation of each individual to its adjacent neighbors, thus appropriately adjusting the individual's density according to its position. Further, a multi-individual deletion mechanism is introduced to improve the efficiency of the strategy. For evaluation purpose, we incorporate the proposed strategy into NSGA-II and test it on ten functions. Our results show that the proposed strategy is able to improve the performance of NSGA-II and the resulting algorithm outperforms related methods to be compared.