title: Many-Objective Particle Swarm Optimization Using Two-Stage Strategy and Parallel Cell Coordinate System.
abstract: It is a daunting challenge to balance the convergence and diversity of an approximate Pareto front in a many-objective optimization evolutionary algorithm. A novel algorithm, named many-objective particle swarm optimization with the two-stage strategy and parallel cell coordinate system (PCCS), is proposed in this paper to improve the comprehensive performance in terms of the convergence and diversity. In the proposed two-stage strategy, the convergence and diversity are separately emphasized at different stages by a single-objective optimizer and a many-objective optimizer, respectively. A PCCS is exploited to manage the diversity, such as maintaining a diverse archive, identifying the dominance resistant solutions, and selecting the diversified solutions. In addition, a leader group is used for selecting the global best solutions to balance the exploitation and exploration of a population. The experimental results illustrate that the proposed algorithm outperforms six chosen state-of-the-art designs in terms of the inverted generational distance and hypervolume over the DTLZ test suite.
authors.forename: Wang
authors.initials: W
authors.lastname: Hu
authors.forename: Gary G
authors.initials: GG
authors.lastname: Yen
authors.forename: Guangchun
authors.initials: G
authors.lastname: Luo