A fast and elitist grid selection evolutionary algorithm for Multi-objective Optimization: GSEA

Multi-objective optimization is an important and challenging topic in the field of industrial design and scientific research. Evolutionary algorithm is a population-based meta-heuristic technique to effectively solve Multi-objective Optimization Problem (MOP). In this paper, a novel EA is proposed, which applied the construction strategy of the elitist population based on spacial grid. In this strategy, firstly, a fast obtaining Pareto set approach with less computation cost is employed; then we filter Pareto set with the grid with the fixed side length to keep the diversity of solutions. Experimental results on test problems show that the GSEA we proposed improves time performance significantly, and is able to find solutions with good diversity and being nearer the true Pareto-optimal front compared to the known NSGA-II, SPEA2 and ε-MOEA.