The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity\r\nowing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the\r\npeak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper,\r\nwe propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of\r\namultistage and a single stage of evolution. In themulti-stage of evolution, individual subswarms evolve independently in parallel,\r\nand in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages\r\nof evolution demonstrate better performance on test functions, especially of higher dimensions.The attractive feature of the PSOPSO\r\nversion of the algorithm is that it does not introduce any new parameters to improve its convergence performance.The strategy\r\nmaintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO.
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