When PSO algorithm calculates function extremum, it often appears premature phenomenon, which leads to large deviation in solving function extremum. However, genetic algorithm uses selection, crossover and mutation operators for function optimization, and directly takes the objective function as the search information in a probabilistic way. Therefore, it enhances the global optimization ability of particle swarm optimization algorithm and speeds up the progress of the algorithm The convergence accuracy is improved.

Filename	Size	Update
Ga_functionmin\circularConstraint.m	265	2013-10-19
Ga_functionmin\gaPeaksExample.m	1427	2013-10-19
Ga_functionmin\peaksPlotIterates.m	6660	2013-10-19
GA和PSO\GA(PSO)\Code.m	381	2013-10-19
GA和PSO\GA(PSO)\Cross.m	1393	2013-10-19
GA和PSO\GA(PSO)\fun.m	106	2013-10-19
GA和PSO\GA(PSO)\GA.m	2221	2013-10-19
GA和PSO\GA(PSO)\Mutation.m	1315	2013-10-19
GA和PSO\GA(PSO)\PSO.m	2187	2013-10-19
GA和PSO\GA(PSO)\Select2.m	801	2013-10-19
GA和PSO\GA(PSO)\test.m	269	2013-10-19
GA和PSO\GA_PSO\Cross.m	1393	2013-10-19
GA和PSO\GA_PSO\fun.m	106	2013-10-19
GA和PSO\GA_PSO\GA_PSO.m	2642	2013-10-19
GA和PSO\GA_PSO\Mutation.m	1315	2013-10-19
GA和PSO\GA_PSO\test.m	269	2013-10-19
GA和PSO\说明.txt	63	2013-10-19
GA和PSO\GA(PSO)	0	2013-10-30
GA和PSO\GA_PSO	0	2013-10-30
Ga_functionmin	0	2013-10-30
GA和PSO	0	2013-10-30

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