I-MOPSO: A Suitable PSO Algorithm for Many-Objective Optimization

Multi-Objective Optimization Problems are problems with more than one objective function. In the literature, there are several Multi-Objective Evolutionary Algorithms (MOE As) that deals with MOPs, including Multi-Objective Particle Swarm Optimization (MOPSO). However, these algorithms scale poorly when the number of objective grows. Many-Objective Optimization researches methods to decrease the negative effect of applying MOE As into problems with more than three objective functions. Here, it is proposed a new PSO algorithm, called I-MOPSO, which explores specific aspects of MOPSO to deal with Many-Objective Problems. This algorithm takes advantage of an archiving method to introduce more convergence and from the strategy of the leader´s selection to introduce diversity on the search. I-MOPSO is evaluated through an empirical analysis aiming to observe how it works in Many-Objective scenarios in terms of convergence and diversity to the Pareto front. The proposed algorithm is compared to other MOE As from the literature through the use of quality indicators and statistical tests.