A new multi-objective particle swarm optimization algorithm based on decomposition

作者：Dai, Cai^*; Wang, Yuping; Ye, Miao

来源：Information Sciences, 2015, 325: 541-557.

DOI：10.1016/j.ins.2015.07.018

• Multi-objective optimization
• Particle swarm optimization
• Artificial intelligence
• Decomposition
• Crowding distance

The diversity of solutions is of great importance for multi-objective evolutionary algorithms. In this paper, a new multi-objective particle swarm optimization algorithm based on decomposition (MPSO/D) is proposed. Firstly, the objective space of a multi-objective problem is decomposed into a set of sub-regions based on a set of direction vectors. Then MPSO/D makes each sub-region have a solution to maintain the diversity. Secondly, considering the convergence of solutions, MPSO/D uses the crowding distance to calculate the fitness values of the reserved solutions for selection operator, and uses the neighboring particles of a particle to determine the global best historical position (gbest) of the particle. The proposed algorithm has been compared with NSGAII, MOEA/D and NNIA on sixteen test sets. The experimental results illustrate that the proposed algorithm outperforms NSGAII, MOEA/D and NNIA in terms of convergence and diversity.

• 出版日期2015-12-20
• 单位陕西师范大学; 西安电子科技大学