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.
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