particle swarm optimization simulation
2016-08-23
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Particle swarm optimization (PSO) is a new branch in the area of evolutionary computation introduced by Kennedy
and Eberhart in 1995 [8]. PSO is easy to implement and has been empirically verified to perform well on many optimization
problems. Because of the success of PSO in solving unconstrained optimization problems, PSO has gradually
gained attention in sloving COPs in the last few years. Liang and Suganthan [9] proposed a dynamic multi-swarm
PSO with a novel constraint-handling mechanism for solving COPs. In their proposal, the population is periodically
and randomly divided into several sub-swarms. In addition, the objective and constraints are assigned to the sub-swarms
adaptively according to the difficulties of the constraints. As a result, the constraints that are more difficult will havemore
sub-swarms working for it, and the constraints that are easier will have less sub-swarms working for it. Moreover, sequential quadr
and Eberhart in 1995 [8]. PSO is easy to implement and has been empirically verified to perform well on many optimization
problems. Because of the success of PSO in solving unconstrained optimization problems, PSO has gradually
gained attention in sloving COPs in the last few years. Liang and Suganthan [9] proposed a dynamic multi-swarm
PSO with a novel constraint-handling mechanism for solving COPs. In their proposal, the population is periodically
and randomly divided into several sub-swarms. In addition, the objective and constraints are assigned to the sub-swarms
adaptively according to the difficulties of the constraints. As a result, the constraints that are more difficult will havemore
sub-swarms working for it, and the constraints that are easier will have less sub-swarms working for it. Moreover, sequential quadr
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