Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
No free lunch theorems for 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
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Particle swarm optimization (PSO) has been applied successfully to a wide range of optimization problems. Appropriate values for control parameters of the particle swarm optimization (PSO) algorithm are critical to its success. This paper proposes that the control parameters of PSO be embedded in the position vector of particles and dynamically adapted while the search is in progress, relieving the user from specifying appropriate values before the search commences. Application of the Self-Adaptive Comprehensive Learning Particle Swarm Optimizer (SACLPSO) to 9 well known test functions show an improvement in performance on most of the functions compared to CLPSO and a tuned PSO.