Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An Empirical Comparison of Particle Swarm and Predator Prey Optimisation
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
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
Particle swarm optimisation with spatial particle extension
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
Extending particle swarm optimisation via genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Digital redesign of uncertain interval systems via a hybrid particle swarm optimiser
International Journal of Innovative Computing and Applications
Expert Systems with Applications: An International Journal
Heterogeneous particle swarm optimizers
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Improved catfish particle swarm optimization with fuzzy adaptation
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Two-layer particle swarm optimization for unconstrained optimization problems
Applied Soft Computing
A rank based particle swarm optimization algorithm with dynamic adaptation
Journal of Computational and Applied Mathematics
Scale-free fully informed particle swarm optimization algorithm
Information Sciences: an International Journal
Example-based learning particle swarm optimization for continuous optimization
Information Sciences: an International Journal
Chaotic particle swarm optimization for data clustering
Expert Systems with Applications: An International Journal
Improved PSO algorithm with harmony search for complicated function optimization problems
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Journal of Medical Systems
An improved quantum-behaved particle swarm optimization algorithm
Applied Intelligence
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Center particle swarm optimization algorithm (CenterPSO) is proposed where a center particle is incorporated into linearly decreasing weight particle swarm optimization (LDWPSO). Unlike other ordinary particles in LDWPSO, the center particle has no explicit velocity, and is set to the center of the swarm at every iteration. Other aspects of the center particle are the same as that of the ordinary particle, such as fitness evaluation and competition for the best particle of the swarm. Because the center of the swarm is a promising position, the center particle generally gets good fitness value. More importantly, due to frequent appearance as the best particle of swarm, it often attracts other particles and guides the search direction of the whole swarm. CenterPSO and LDWPSO are extensively compared on three well-known benchmark functions with 10, 20, 30 dimensions. Experimental results show that CenterPSO achieves not only better solutions but also faster convergence. Furthermore, CenterPSO and LDWPSO are compared as neural network training algorithms. The results show that CenterPSO achieves better performance than LDWPSO.