A Many-Objective Particle Swarm Optimization Based On Virtual Pareto Front

A many-objective problems (MaOP) refer to the optimization problem involving more than three objectives. Particle swarm optimization (PSO) is one of the potential heuristic methods suited for solving MaOPs. The personal best selection strategy, the global best selection strategy, and the archive maintenance strategy are the three key components in the design of a Many-Objective Particle Swarm Optimization (MaOPSO). The personal best and global best selection strategies determine the direction where particles will fly. The archive maintenance strategy has an important impact on convergence and diversity of its algorithm. In MaOPs, the high dimensionality in the objective space decreases the probability of a solution to be dominated by the other solutions in the population. Thus, it becomes more difficult for PSO to select the good leaders from so many non-dominated solutions. In this paper, a virtual Inverted Generational Distance indicator is proposed to evaluate the comprehensive quality of a solution in the external archive according to a constructed virtual Pareto front (vPF). Accordingly, a new indicator-based MaOPSO using vPF (MaOPSO/vPF) is developed to improve the convergence and diversity of the approximate Pareto front. Experimental results on the MaF test suites demonstrate that the proposed MaOPSO/vPF performs better than some selected competing Multi-objective Optimization Evolutionary Algorithms.