Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Numerical Optimization Using Organizational Evolutionary Algorithms
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Multimodality Image Registration by Particle Swarm Optimization of Mutual Information
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
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The classical particle swarm optimization (PSO) has its own disadvantages, such as low convergence speed and prematurity. All of these make solutions have probability to convergence to local optimizations. In order to overcome the disadvantages of PSO, an organizational particle swarm algorithm (OPSA) is presented in this paper. In OPSA, the initial organization is a set of particles. By competition and cooperation between organizations in every generation, particles can adapt the environment better, and the algorithm can converge to global optimizations. In experiments, OPSA is tested on 6 unconstrained benchmark problems, and the experiment results are compared with PSO_TVIW, MPSO_TVAC, HPSO_TVAC and FEP. The results indicate that OPSA performs much better than other algorithms both in quality of solutions and in computational complexity. Finally, the relationship between parameters and success ratio are analyzed.