Dominating Global Best Selection for Multi-objective Particle Swarm Optimization
For multi-objective particle swarm optimization,the selection of the global best becomes an interesting topic,because it balances convergence and diversity.But the global best selected by the existing strategies has a high probability of not dominating the particle.The flight towards the global best not dominating the particle is expected to cause some objectives to become worse,thus does not surely promote convergence.As the accumulation of the flights,the algorithm suffers from slow convergence.Therefore we propose the dominating strategy to accelerate the convergence by decreasing that probability.Experimental results show our strategy outperforms other strategies.
multi-objective optimization problem particle swarm optimization global best pareto dominance archive
Heming Xu Yinglin Wang Xin Xu
Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai,China
国际会议
杭州
英文
1504-1507
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）