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 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
Artificial Intelligence Review
QoS multicast routing using a quantum-behaved particle swarm optimization algorithm
Engineering Applications of Artificial Intelligence
Design IIR digital filters using quantum-behaved particle swarm optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
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Particle Swarm Optimization (PSO) is a population-based evolutionary search technique, which has comparable performance with Genetic algorithm. The existing PSOs, however, are not global-convergence-guaranteed algorithms, because the evolution equation of PSO, make the particle only search in a finite sampling space. In [10,11], a Quantum-behaved Particle Swarm Optimization algorithm is proposed that outperforms traditional PSOs in search ability as well as having less parameter. This paper focuses on discussing how to select parameter when QPSO is practically applied. After the QPSO algorithm is described, the experiment results of stochastic simulation are given to show how the selection of the parameter value influences the convergence of the particle in QPSO. Finally, two parameter control methods are presented and experiment results on the benchmark functions testify their efficiency.