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.