EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
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
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
A new quantum behaved particle swarm optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Quantum-Behaved Particle Swarm Optimization with Chaotic Search
IEICE - Transactions on Information and Systems
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Quantum mechanics inspired Particle Swarm Optimisation for global optimisation
International Journal of Artificial Intelligence and Soft Computing
A quantum particle swarm optimization used for spatial clustering with obstacles constraints
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
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In this paper, the mutation mechanism is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm and then effectively escape from local minima to increase its global search ability. Based on the characteristic of QPSO algorithm, the two variables, global best position (gbest) and mean best position (mbest), are mutated with Cauchy distribution respectively. Moreover, the amend strategy based on annealing is adopted by the scale parameter of mutation operator to increase the self-adaptive capability of the improved algorithm. The experimental results on test functions showed that QPSO with gbest and mbest mutation both performs better than PSO and QPSO without mutation.