Abstract-many problems in the real world are dynamic in which the environment changes. However, the nature itself provides solutions for adaptation to these changes in order to gain the maximum benefit, i.e. finding the global optimum, at any moment. One of these solutions is hibernation of animals when food is scarce and an animal may use more energy in searching for food than it would receive from consuming the food. In this paper, we applied the idea of hibernation in a multi-swarm optimization algorithm, in which a parent swarm explores the search space and child swarms exploit promising areas found by the parent swarm. In the proposed model, whenever the search efforts of a child swarm for exploiting an area becomes unproductive, the child swarm hibernates. Similar to the nature, which the change of the season awakens hibernating animals, in the proposed model hibernating swarms are awakened upon the detection of a change in the environment. Experimental results on various dynamic environments modeled by the moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including similar particle swarm algorithms for dynamic environments like mQSO, adaptive mQSO, and FMSO.
Abstract. Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarms to exploit promising areas found by the parent swarm. To improve the search performance, when the search areas of two child swarms overlap, the worse child swarms will be removed. Moreover, in order to quickly track the changes in the environment, all particles in a child swarm perform a random local search around the best position found by the child swarm after a change in the environment is detected. Experimental results on different dynamic environments modelled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including FMSO, a similar particle swarm algorithm for dynamic environments, for all tested environments.
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