Survey on particle swarm optimization based clustering analysis
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
An improved particle swarm optimisation based on cellular automata
International Journal of Computing Science and Mathematics
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For the problem of particle swarm optimization parameters selection, a kind of intelligent method to optimum parameters selection using another particle swarm optimization algorithm is proposed. Firstly it analyzes the effect of each parameter on algorithm performance in detail. Then it takes parameter selection of PSO algorithm as a complex optimization problem, sets appropriate fitness function to describe optimization performance, and uses PSO-PARA algorithm to optimize the parameters selection method of PSO-OPT algorithm. Tests to the benchmark function show that these parameters are better than the experience parameters test results in the optimal fitness, the mean value of optimal fitness, convergence rate.