A Hybrid Leader Selection Strategy for Many-Objective Particle Swarm Optimization

Many existing Multi-objective Particle Swarm Optimizers (MOPSOs) may encounter difficulties for a set of good approximated solutions when solving problems with more than three objectives. One possible reason is that the diluted selection pressure causes MOPSOs to fail to generate a set of good approximated Pareto solutions. In this paper, a new approach called the Hybrid Global Leader Selection Strategy (HGLSS) is proposed to deal with many-objective problems more effectively. HGLSS provides two global leader selection mechanisms: one for exploration and one for exploitation. Each particle (solution) can choose one of these two leader selection schemes to identify its global best leader. An external archive is adopted for maintaining the diversity of the found solutions and it contains the final solution reported at the end of the run. The update of the external archive is based on both Pareto dominance and density estimation. The performance of the proposed approach is compared with respect to nine state-of-the-art multi-objective metaheuristics in solving several benchmark problems. Our results indicate that the proposed algorithm generally outperforms the others in terms of Modified Inverted Generational Distance (IGD(+;)) indicator.