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
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
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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A parameter automation strategy for particle swarm optimization (PSO) is introduced to enhance the performance to solve high dimensions objects. Initially, to maintain the diversities of the population, the concept of "individual coefficients" (IC) is employed, where each particle has the individual inertia weight and social acceleration coefficient. From the basis of IC, The "individual coefficients" particle swarm optimization with simulated annealing (PSO-ICSA) is proposed, where two new strategies are discussed to adjust the coefficients self-adaptively. First, the inertia weights and social acceleration coefficients are adjusted by evaluating the adaptive values of the just passed evolution at each iteration step, while the cognitive acceleration coefficient varies linearly with time. Second, a simulated annealing mutation strategy (SA) is combined to enhance the global convergence ability. The test on benchmark problems shows that the proposed method is more effective, reliable and insensitive to dimensions than the existed time-varying coefficients methods especially of high dimensions objects.