Knowledge and Information Systems
Development of immunized PSO algorithm and its application to Hammerstein model identification
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Hybrid particle swarm optimization algorithm with fine tuning operators
International Journal of Bio-Inspired Computation
Improved identification of Hammerstein plants using new CPSO and IPSO algorithms
Expert Systems with Applications: An International Journal
Parameter estimation in dynamic biochemical systems based on adaptive particle swarm optimization
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Parameter estimation in dynamic biochemical systems based on adaptive particle swarm optimization
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
A review on particle swarm optimization algorithms and their applications to data clustering
Artificial Intelligence Review
International Journal of Applied Evolutionary Computation
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This paper presents a few new competitive approaches to particle swarm optimization (PSO) algorithm in terms of the global and local best values (GLbest-PSO) and the standard PSO along with three set of variants namely, inertia weight (IW), acceleration co-efficient (AC) and mutation operators in this paper. Standard PSO is designed with time varying inertia weight (TVIW) and either time varying AC (TVAC) or fixed AC (FAC) while GLbest-PSO comprises of Global-average Local best IW (GaLbestIW) with either Global-Local best AC (GLbestAC) or FAC. The performances of these two algorithms are improved considerably in solving an optimal control problem, by introducing the concept of mutation variants between particles in each generation. The presence of mutation operator sharpens the convergence and tunes to the best solution. In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate the improved performances of the proposed PSOs over the standard PSOs.