面向大规模全局优化的随机社会认知分层粒子群优化器

H. Ge, Z. Ma, Liang Sun
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引用次数: 2

摘要

本文提出了一种具有随机社会认知的分层粒子群优化器,简称为HPSO-RSC。在HPSO-RSC的执行过程中,社会环境是动态变化的,每个粒子不仅被其先前的最佳粒子和整个种群的全局最佳粒子所吸引,而且还被其他所有更好的粒子随机吸引。在执行过程的早期,为了加快算法的收敛速度,粒子倾向于选择全局最优粒子作为认知对象。另一方面,在执行过程的后期,为了保持种群的多样性,粒子倾向于选择比自己更好的粒子作为认知对象。为了解决大规模全局优化问题,将该算法集成到一个具有高效变量交互检验方法的协同进化框架中。在CEC 2008基准测试上进行了模拟实验。结果表明,对于大多数基准问题,HPSO-RSC具有较强的全局寻优能力。
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A hierarchical particle swarm optimizer with random social cognition for large scale global optimization
In this paper, a Hierarchical Particle Swarm Optimizer with Random Social Cognition, briefly expressed as HPSO-RSC, is proposed. During the execution process of HPSO-RSC, the social environment is changed dynamically, and each particle is not only attracted by its previous best particle and the global best particle of the whole population, but also attracted by all other better particles randomly. During the early stage of the execution process, to speed up convergence of the algorithm, the particles are inclined to choose the global best particle as cognition object. On the other hand, during the late stage of the execution process, to keep the diversity of the population, the particles are inclined to choose the particles that better than themselves as cognition object. To solve the large scale global optimization problem, the algorithm is integrated into a cooperative coevolution framework with an efficient variable interaction checking method. Simulated experiments were conducted on the CEC'2008 benchmarks. The result demonstrates that, HPSO-RSC has strong ability to find the global optimum for most of the benchmark problems.
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