Description: For constrained optimization problems, most algorithms are based on the concept of gradient, requires that the target function and constraints differentiable, and generally can only be obtained locally optimal solution ' PSO (@ DMLEP & H 6VDMS .RLESDTEL F, Acronym @ 6 .) [ )], because of its easy to understand, easy to implement, does not require the objective function and constraints differentiable, and are able to obtain greater probability characteristics of the global optimum solution, has been obtained in a number of optimization problems successful application
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File list (Check if you may need any files):
PSOt\ackley.m
....\alpine.m
....\DeJong_f2.m
....\DeJong_f3.m
....\DeJong_f4.m
....\DemoPSOBehavior.m
....\demoPSOnet.m
....\f6.m
....\f6mod.m
....\f6_bubbles_dyn.m
....\f6_linear_dyn.m
....\f6_spiral_dyn.m
....\forcecol.m
....\forcerow.m
....\Foxhole.m
....\goplotpso.m
....\goplotpso4demo.m
....\goplotpso4net.m
....\Griewank.m
....\license.txt
....\linear_dyn.m
....\NDparabola.m
....\normmat.m
....\pso_neteval.m
....\pso_Trelea_vectorized.m
....\PSO工具箱使用简介.doc
....\Rastrigin.m
....\ReadME.txt
....\Rosenbrock.m
....\spiral_dyn.m
....\trainpso.m
....\tripod.m
PSOt
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