Swarm intelligence
Designing Neural Networks Using PSO-Based Memetic Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Novel binary encoding differential evolution algorithm
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Hybrid particle swarm optimization for flow shop scheduling with stochastic processing time
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Blending scheduling under uncertainty based on particle swarm optimization with hypothesis test
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
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Evolving artificial neural network is an important issue in both evolutionary computation (EC) and neural networks (NN) fields. In this paper, a hybrid particle swarm optimization (PSO) is proposed by incorporating differential evolution (DE) and chaos into the classic PSO. By combining DE operation with PSO, the exploration and exploitation abilities can be well balanced, and the diversity of swarms can be reasonably maintained. Moreover, by hybridizing chaotic local search (CLS), DE operator and PSO operator, searching behavior can be enriched and the ability to avoid being trapped in local optima can be well enhanced. Then, the proposed hybrid PSO (named CPSODE) is applied to design multi-layer feed-forward neural network. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed hybrid PSO.