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
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Stability Analysis and Parameter Selection of a Particle Swarm Optimizer in a Dynamic Environment
EMS '08 Proceedings of the 2008 Second UKSIM European Symposium on Computer Modeling and Simulation
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
Stability analysis of the particle dynamics in particle swarm optimizer
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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This paper introduces an improved particle swarm optimizer with leadership (PSO-L), inspired by the effect of individual experience to group in nature. Firstly, the stability analysis of an individual particle is undertaken, using Lyapunov theory. The obtained results offer a more stringent convergence condition on parameter selection in comparison with the existing results. Next, based on the convergence condition, the method PSO-L is proposed. In the method, to ensure that the swarm converges to the global optimum solution rapidly, a particle is selected as the leader of the swarm during the exploration search. And the parameter values of the leader particle in iteration are selected according to the obtained convergence condition. Then, the effect of the convergence condition to single particle's trajectory is demonstrated. And five benchmark functions are used to verify the feasibility of the improved method, compared with two famous PSO methods. Finally, an application example is given to show the improved performance of the method.