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
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Tracking Multiple Optima in Dynamic Environments by Quantum-Behavior Particle Swarm Using Speciation
International Journal of Swarm Intelligence Research
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In this paper, we focus on solving non-linear programming (NLP) problems using quantum-behaved particle swarm optimization (QPSO). After a brief introduction to the original particle swarm optimization (PSO), we describe the origin and development of QPSO, and the penalty function method for constrained NLP problems. The performance of QPSO is tested on some unconstrained and constrained benchmark functions and compared with PSO with inertia weight (PSO-In) and PSO with constriction factor (PSO-Co). The experimental results show that QPSO outperforms the traditional PSOs and is a promising optimization algorithm.