Particle swarm optimization algorithm based on dynamic memory strategy

This paper mainly studies the influence of memory on individual performance in particle swarm system. Based on the observation of social phenomenon from the perspective of social psychology, the concept of individual memory contribution is defined and several measurement methods to determine the level of effect of individual memory on its behavior are discussed. A dynamic memory particle swarm optimization algorithm is implemented by dynamically assigning appropriate weight to each individual's memory according to the selected metrics values. Numerical experiment results on benchmark optimization function set show that the proposed scheme can effectively adjust the weight of individual memory according to different optimization problems adaptively. Numerical results also demonstrate that dynamic memory is an effective improvement strategy for preventing premature convergence in particle swarm optimization algorithm.

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