Normal Attractor Intersection Based Multi-objective Optimization Using Particle Swarm Optimization

Abstract

Frequently, multiobjective optimization problems are solved using non-dominated based evolutionary algorithms or gradient-based methods. In the last years, successful proposals that combine the two approaches have been developed. In this work, we propose the Normal Attractor Intersection (NAI) and the NAImopso. The NAI avoids the a priori definition of the search direction and the equality constraints; it uses a set of attractors that cover the entire Pareto Front to generate solutions in the Pareto front. The NAImopso is a multiobjective optimization algorithm based on decomposition; we used it to prove the ability of the NAI to obtain the Pareto front. We compared our proposal against four state-of-the-proposals, and it was evaluated using three well-recognized indicators as performance metrics, the hypervolume indicator, the coverage, and the ε-indicator. The experimental results showed that solutions obtained with the NAImopso were better than the solutions obtained with the other algorithms with it was compared.