Extending self-organizing particle systems to problem solving
Artificial Life
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Nonlinear blind source separation using higher order statistics anda genetic algorithm
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
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First, based on the particle swarm optimization, an extended particle swarm optimizer with acceleration coefficients (EPSO_AAC) is presented. The personal best particle is replaced by the average of personal best particles in swarm at generation, and time-varying acceleration coefficients are applied by establishing a nonlinear functional relationship between acceleration coefficients and the different of the average fitness of all particles and the fitness of the global best particle. The proposed algorithm uses more particles' information, and adjusts adaptively “cognition” component and “social” component by time-varying acceleration coefficients, thus improves convergence performance. Then, the proposed algorithm is applied to nonlinear blind source separation. The demixing system of the nonlinear mixtures is modeled using a multi-input multi-output B-spline neural network whose weights are optimized under the criterion of independence of its outputs by EPSO_AAC. The experiment results demonstrate that the proposed algorithms are effective, and have good convergence performance.