Computational intelligence PC tools
Computational intelligence PC tools
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
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
An analysis of particle swarm optimizers
An analysis of particle swarm optimizers
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Artificial intelligence methodologies for agile refining: an overview
Knowledge and Information Systems
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Knowledge and Information Systems
Hybrid particle swarm optimization algorithm with fine tuning operators
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
Fuzzy based Impulse Noise Reduction Method
Multimedia Tools and Applications
Multi-region particle swarm optimisation algorithm
International Journal of Computer Applications in Technology
Quantum and impulse noise filtering from breast mammogram images
Computer Methods and Programs in Biomedicine
International Journal of Applied Evolutionary Computation
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This paper presents a new and improved version of particle swarm optimization algorithm (PSO) combining the global best and local best model, termed GLBest-PSO. The GLBest-PSO incorporates global–local best inertia weight (GLBest IW) with global–local best acceleration coefficient (GLBest Ac). The velocity equation of the GLBest-PSO is also simplified. The ability of the GLBest-PSO is tested with a set of bench mark problems and the results are compared with those obtained through conventional PSO (cPSO), which uses time varying inertia weight (TVIW) and acceleration coefficient (TVAC). Fine tuning variants such as mutation, cross-over and RMS variants are also included with both cPSO and GLBest-PSO to improve the performance. The simulation results clearly elucidate the advantage of the fine tuning variants, which sharpen the convergence and tune to the best solution for both cPSO and GLBest-PSO. To compare and verify the validity and effectiveness of the GLBest-PSO, a number of statistical analyses are carried out. It is also observed that the convergence speed of GLBest-PSO is considerably higher than cPSO. All the results clearly demonstrate the superiority of the GLBest-PSO.