Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
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
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A note on the empirical evaluation of intermediate recombination
Evolutionary Computation
Incorporating psychology model of emotion into ant colony optimization algorithm
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
A novel bee swarm optimization algorithm with chaotic sequence and psychology model of emotion
ISTASC'09 Proceedings of the 9th WSEAS International Conference on Systems Theory and Scientific Computation
Predicted modified PSO with time-varying accelerator coefficients
International Journal of Bio-Inspired Computation
Psychological model of particle swarm optimization based multiple emotions
Applied Intelligence
The Visual Computer: International Journal of Computer Graphics
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This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model, each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards the sense it got from both its own history and other particle. The sense factor is calculated by famous Weber-Fechner Law. All these psychology factors will influence the next action of the particle. The resulting algorithm, known as Emotional PSO (EPSO), is shown to perform significantly better than the original PSO algorithm on different benchmark optimization problems. Avoiding premature convergence allows EPSO to continue search for global optima in difficult multimodal optimization problems, reaching better solutions than PSO with a much more fast convergence speed.