The particle swarm optimization (PSO) is a recently invented evolutionary computation technique which is gaining popularity owing to its simplicity in implementation and rapid convergence. In the case of single-peak functions, PSO rapidly converges to the peak; however, in the case of multimodal functions, the PSO particles are known to get trapped in the local optima. In this paper, we propose a variation of the algorithm called parallel swarms oriented particle swarm optimization (PSO-PSO) which consists of a multistage and a single stage of evolution. In the multi-stage of evolution, individual subswarms evolve independently in parallel, and in the single stage of evolution, the sub-swarms exchange information to search for the global-best. The two interweaved stages of evolution demonstrate better performance on test functions, especially of higher dimensions. The attractive feature of the PSO-PSO version of the algorithm is that it does not introduce any new parameters to improve its convergence performance. The strategy maintains the simple and intuitive structure as well as the implemental and computational advantages of the basic PSO. 1. Introduction Evolutionary algorithms (EAs) are increasingly being applied to solve the problems in diverse domains. These metaheuristic algorithms are found to be successful in many domains chiefly because of their domain-independent evolutionary mechanisms. Evolutionary computation is inspired by biological processes which are at work in nature. Genetic algorithm (GA) [1] modeled on the Darwinian evolutionary paradigm is the oldest and the best known evolutionary algorithm. It mimics the natural processes of selection, crossover, and mutation to search for optimum solutions in massive search spaces. Particle swarm optimization (PSO) is a recently developed algorithm belonging to the class of biologically inspired methods [2–9]. PSO imitates the social behavior of insects, birds, or fish swarming together to hunt for food. PSO is a population-based approach that maintains a set of candidate solutions, called particles, which move within the search space. During the exploration of the search space, each particle maintains a memory of two pieces of information: the best solution (pbest) that it has encountered so far and the best solution (gbest) encountered by the swarm as a whole. This information is used to direct the search. Researchers have found that PSO has the following advantages over the other biologically inspired evolutionary algorithms: (1) its operational principle is very simple and intuitive; (2)
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