Enhanced Multi-objective Evolutionary Algorithms Using Local Dominance


In this paper, we propose a calculation method of local dominance and enhance multiobjective evolutionary algorithms by performing a distributed search based on local dominance. We divide the population into several sub-populations by using declination angles of polar coordinate vectors in the objective space. We calculate local dominance for individuals belonging to each sub-population based on the local search direction, and apply genetic operators to individuals within each sub-population. We verify the effectiveness of the proposed method by comparing the search performance between NSGA-II, SPEA2 and their enhanced versions.