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
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 - 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
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Based on the analysis of biological symbiotic relationship, the mechanism of facultative parasitic behaviour is embedded into the particle swarm optimization (PSO) to construct a two-population PSO model called PSOPB, composed of the host and the parasites population In this model, the two populations exchange particles according to the fitness sorted in a certain number of iterations In order to embody the law of "survival of the fittest" in biological evolution, the poor fitness particles in the host population are eliminated, replaced by the re-initialization of the particles in order to maintain constant population size The results of experiments of a set of 6 benchmark functions show that presented algorithm model has faster convergence rate and higher search accuracy compared with CPSO, PSOPC and PSO-LIW.