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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(More)
— 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(More)
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