Application of modified NSGA-II algorithm to multi-objective reactive power planning
Applied Soft Computing
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A Multiobjective Particle Swarm Optimizer for Constrained Optimization
International Journal of Swarm Intelligence Research
INSPM: An interactive evolutionary multi-objective algorithm with preference model
Information Sciences: an International Journal
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In multi-objective evolutionary algorithms (MOEAs), the diversity of Pareto front (PF) is significant. For good diversity can provide more reasonable choices to decision-makers. The diversity of PF includes the span and the uniformity. In this paper, we proposed a dynamic crowding distance (DCD) based diversity maintenance strategy (DMS) (DCD-DMS), in which individual’s DCD are computed based on the difference degree between the crowding distances of different objectives. The proposed strategy computes individuals’ DCD dynamically during the process of population maintenance. Through experiments on 9 test problems, the results demonstrate that DCD can improve diversity at a high level compared with two popular MOEAs: NSGA-II and ε-MOEA.