Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A note on the learning automata based algorithms for adaptive parameter selection in PSO
Applied Soft Computing
Computational Optimization and Applications
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Recently the particle swarm optimization (PSO) has been used in many engineering applications, which operate in dynamic environment and has proved its competitiveness over genetic algorithmin many natural number approaches. In the state of the art, it is assumed that all the particles have the same parameters, while in the real world; each individual has its own character, which means each particle has different parameters. In this paper, we study the feasibility and the behavior of local parameters for each particle in the PSO, and control the parameters by a simple algorithm. More advanced control algorithm can be applied to improve the search. Adjusting our PSO for different applications is easier as the swarm parameters are adjusted automatically for each particle. However, this modification of PSO can be applied for any type of PSO to improve it. As an example, we apply it to the hierarchical particle swarm optimization (HPSO). The results are obtained in static and dynamic environments. Local approach with a naive controller overcomes the other approaches in most of the cases.