Abstract
Particle Swarm Optimization (PSO) algorithm is considered as one of the crowd intelligence optimization algorithms. Dynamic optimization problems in which change(s) may happen over the time are harder to manage than static optimization problems. In this paper an algorithm based on PSO and memory for solving dynamic optimization problems has been proposed. The proposed algorithm uses the memory to store the aging best solutions and uses partitioning for preventing convergence in the population. The proposed approach has been tested on moving peaks benchmark (MPB). The MPB is a suitable problem for simulating dynamic optimization problems. The experimental results on the moving peaks benchmark show that the proposed algorithm is superior to several other well-known and state-of-the-art algorithms in dynamic environments.
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