Multi-objective particle swarm optimizer with opposition-based learning

作者：Ma Mingyang^*; He Yizhao; Xie Yilun; Zhao Xinchao

来源：Journal of Computational Information Systems, 2013, 9(18): 7165-7172.

DOI：10.12733/jcis6324

• Experimental comparison
• Multi-objective particle swarm optimizer
• Opposition-based learning
• Optimization problems
• Pareto-optimal sets
• Particle swarm optimizers
• Population initializations
• Uniform distribution

Particle swarm optimization (PSO) has shown to yield good performance for solving various optimization problems. In this paper, a Multi-objective Particle Swarm Optimizer (MOLPSO) is proposed with Opposition-based Learning. This algorithm uses opposition-based learning strategy for population initialization and evolution. Experimental comparison with NSGA-II are conducted with several multi-objective benchmarks, which indicate that even more uniform distributions along the Pareto front and even better coverage of the Pareto optimal sets can be obtained.

• 出版日期2013
• 单位北京大学