Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
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
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Particle Swarm Optimization (PSO) has received increased attention in the evolutionary computation fields recently. In the paper, we proposed Adaptive constriction factor for Location-related Particle Swarm (ALPS) that is shown to be superior when compared with the existing PSO algorithm. We adapt a technique of overcoming complex problems with PSO. This is accomplished by using the ratio of the relative location of better particles to determine the direction in which each constriction factor of the particle needs to be varied. Finally, we are presented experiment results on benchmark functions testify ALPS's efficiency.