Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to difference equations
An introduction to difference equations
Computer-controlled systems (3rd ed.)
Computer-controlled systems (3rd ed.)
Stability for time varying linear dynamic systems on time scales
Journal of Computational and Applied Mathematics
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Information Processing Letters
Journal of Global Optimization
Performance based unit loading optimization using particle swarm optimization approach
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Engineering Applications of Artificial Intelligence
WSEAS TRANSACTIONS on SYSTEMS
Information Processing Letters
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
An application of swarm optimization to nonlinear programming
Computers & Mathematics with Applications
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
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
Particle swarm optimization models applied to neural networks using the R language
WSEAS TRANSACTIONS on SYSTEMS
Particle swarm optimize fuzzy logic memberships of AC-Drive
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
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In this paper an extensive theoretical and empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The convergence of the classical PSO algorithm is addressed in detail. The conditions that should be imposed on parameters of the algorithm in order for it to converge in mean-square have been derived. The practical implications of these conditions have been discussed. Based on these implications a novel, recently proposed parameterization scheme for the PSO has been introduced. The novel optimizer is tested on an extended set of benchmarks and the results are compared to the PSO with time-varying acceleration coefficients (TVAC-PSO) and the standard genetic algorithm (GA).