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中文摘要：
综合学习粒子群算法（CLPSO）能够改善粒子群算法多样性差且易局部收敛的问题,相比传统PSO算法能够一定程度避免算法早熟,但却存在收敛速度慢的问题。对此,提出一种CLPSO的改进算法（CLPSO-II）,为每个粒子随机构造两个学习粒子,引入测评机制,择优学习。实验结果表明,CLPSO-II能有效提高CLPSO的搜索效率,在处理多峰函数时,其性能优于传统粒子群算法（PSO）、全面学习粒子群算法（FIPS）和综合学习粒子群算法（CLPSO）。
英文摘要：
The comprehensive learning particle swarm optimizer（CLPSO）proposed in the literature has successfully improved the population diversity of traditional PSO so as to avoid the premature convergence to some extent.However,the algorithm encounters the problem of slow convergence speed,especially during the later stage of search process.An improved version of CLPSO（termed CLPSO-II）was proposed,which constructed two exemplars for each particle and choe the one with better fitness to learn according to an exemplar evaluation strategy.Experimental result shows that CLPSO-II improves the search efficiency of CLPSO,and it comprehensively outperforms traditional PSO,FIPS and CLPSO when dealing with multimodal functions.
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