Local Dominance Using Polar Coordinates to Enhance Multiobjective Evolutionary Algorithms


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. In this method, we first transform all fitness vectors of individuals to polar coordinate vectors in the objective function space. Then we divide the population into several sub-populations by using declination angles. We calculate local dominance for individuals belonging to each sub-population based on the local search direction, and apply selection, recombination, and mutation to individuals within each sub-population. We pick up NSGA-II and SPEA2 as two representatives of the latest generation of multiobjective evolutionary algorithms and enhance them with our method. We verify the effectiveness of the proposed method obtaining Pareto optimal solutions satisfying diversity conditions by comparing the search performance between the conventional algorithms and their enhanced versions.