Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Swarm intelligence
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
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
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
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A note on the learning automata based algorithms for adaptive parameter selection in PSO
Applied Soft Computing
A new expansion of cooperative particle swarm optimization
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Information entropy and interaction optimization model based on swarm intelligence
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
A multi-population cooperative particle swarm optimizer for neural network training
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A novel particle swarm optimizer using optimal foraging theory
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
Taguchi-Particle Swarm Optimization for Numerical Optimization
International Journal of Swarm Intelligence Research
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Inspired by the phenomenon of symbiosis in natural ecosystem, a master-slave mode is incorporated into Particle Swarm Optimization (PSO), and a Multi-population Cooperative Optimization (MCPSO) is thus presented. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute PSO (or its variants) independently to maintain the diversity of particles, while the master swarm enhances its particles based on its own knowledge and also the knowledge of the particles in the slave swarms. In the simulation part, several benchmark functions are performed, and the performance of the proposed algorithm is compared to the standard PSO (SPSO) to demonstrate its efficiency.