The role of ε-dominance in multi objective particle swarm optimization methods

In this paper, the influence of ε-dominance on multi-objective particle swarm optimization (MOPSO) methods is studied. The most important role of ε-dominance is to bound the number of non-dominated solutions stored in the archive (archive size), which has influences on computational time, convergence and diversity of solutions. Here, ε-dominance is compared with the existing clustering technique for fixing the archive size and the solutions are compared in terms of computational time, convergence and diversity. A new diversity metric is also suggested. The results show that the ε-dominance method can find solutions much faster than the clustering technique with comparable and even in some cases better convergence and diversity.