Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
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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.