Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Comparison between Genetic Algorithms and Particle Swarm Optimization
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
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Based on the previously introduced Quantum-behaved Particle Swarm Optimization (QPSO), a revised QPSO with Gaussian disturbance on the mean best position of the swarm is proposed. The reason for the introduction of this novel method is that the disturbance can effectively prevent the stagnation of the particles and therefore make them escape the local optima and sub-optima more easily. Before proposing the Revised QPSO (RQPSO), we introduce the origin and the development of the original PSO and QPSO. To evaluate the performance of the new method, the Revised QPSO, along with QPSO and Standard PSO, is tested on several well-known benchmark functions. The experimental results show that the Revised QPSO has better performance than QPSO and Standard PSO generally.