Comparison between Genetic Algorithms and 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
Quantum-Behaved Particle Swarm Optimization with Mutation Operator
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Quantum-Behaved particle swarm optimization with immune operator
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Quantum-Behaved particle swarm optimization with adaptive mutation operator
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Improving quantum-behaved particle swarm optimization by simulated annealing
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Parameter selection of quantum-behaved particle swarm optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.