Hybrid Sampling Strategy-based Multiobjective Evolutionary Algorithm

Recently more research works are focused on multiobjective evolutionary algorithm (MOEA) duo to its ability of global and local search for solving multiobjective optimization problem (MOOP) and ability to provide more practical solutions to decision maker; however, most of existing MOEAs cannot achieve satisfactory results in both quality and computational speed. This paper proposes a hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MOEA) to deal with such problem. HSS-MOEA tactfully combines the sampling strategy of vector evaluated genetic algorithm (VEGA) and the sampling strategy according to a new Pareto dominating and dominated relationship-based fitness function (PDDR-FF). The sampling strategy of VEGA prefers the edge area of the Pareto front and PDDR-FF-based sampling strategy has the tendency converging toward the central area of the Pareto front. The hybrid sampling strategies preserve both the convergence rate and the distribution performance. Numerical comparisons show that HSS-MOEA could get the better convergence performance, slightly better or equivalent distribution performance, and obviously better efficiency than existing MOEAs