Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
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
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Self-organizing potential field network: a new optimization algorithm
IEEE Transactions on Neural Networks
Distributed Query Plan Generation using Particle Swarm Optimization
International Journal of Swarm Intelligence Research
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This paper proposes an Adaptive Comprehensive Learning Particle Swarm Optimizer with History Learning (AH-CLPSO) based on the previous proposed Learning Particle Swarm Optimizer (CLPSO) [1], which is good at multimodal problems but converges slow on single modal problems. A self-adaptation technique is introduced to adjust the learning probability adaptively in the search process and the historical information is used in the velocity update equation to search more effectively. The experiment results show that the history learning strategy and the adaptation technique improves the performance of CLPSO on problems which need fast convergence and achieve comparable results on the problems requiring slow convergence.