Self-Controlling Dominance Particle Swarm Optimization

A well-known problem faced by Pareto Based Multi Objective Optimization Algorithms, including Multi-Objective Particle Swarm Optimization (MOPSO) algorithms is the deterioration of its search ability when the number of objectives scales up. In the literature some techniques were proposed to overcome these limitations, between them the modification of the domination relation. This work aims to propose a new MOPSO algorithm called SCDAS-SMPSO which uses the Self-Controlling Dominance Area of Solutions (S-CDAS) technique to modify the domination relation and increase the selection pressure needed in many-objective optimization. The new MOPSO is compared through experiments with other two MOPSOs in terms of convergence and diversity in many-objective scenarios. The results were analyzed through different quality indicators and statistical tests.