Evolutionary algorithm with dynamic population size for multi-objective optimization

Abstract

Presents an incremental multiobjective evolutionary algorithm with dynamic population size that is adaptively computed according to the online discovered trade-off surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine-tuning for broader neighborhood exploration to achieve better convergence as well as discovering any gaps or missing trade-off regions at each generation. The effectiveness of the proposed methodology is validated upon a benchmark multiobjective optimization problem.