Description: Nonlinear constrained multi-objective particle swarm algorithm, using gray theory, chaos theory, dynamic penalty function can be optimized for any complex function, the effect is very good
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GS_CH_MOPSO_Grey\AbsoluteGreyIncidence.m
................\BtypeGreyIncidence.m
................\CtypeGreyIncidence.m
................\DengTypeGreyIncidence.m
................\discrete.asv
................\discrete.m
................\emigrant_chao.asv
................\emigrant_chao.m
................\hs_err_pid5740.log
................\ImproveAbsoluteGreyIncidence.m
................\ImproveRelativeGreyIncidence.asv
................\ImproveRelativeGreyIncidence.m
................\Pso_Object.m
................\Pso_Opt.m
................\Pso_Opt_fun_1.m
................\Pso_Opt_fun_2.m
................\Pso_restrain.m
................\RelativeGreyIncidence.m
GS_CH_MOPSO_Grey
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