Multimodal Function Optimization Using an Improved Swarm Optimizer

In multimodal optimization,convergence of the basic particle swarm optimizer (BPSO) is relatively slow at the late evo- lution.And,particle with the best fitness may fluctuate around the globally-optimal solution,which decreases optimization precision. Therefore,an improved swarm optimizer with controllable velocity factor is proposed.On the basis of the def'mition of three strate- gies for velocity control of evolved particles,i.e.the completely random one,the partial controllable one and the completely control- lable one,optimization precision and computation expense of the modified optimizers are researched comparatively by using several tracks for optimization with different velocity-changing features.Experiments show that performance of the BPSO algorithm is im- proved to some extent by these controllable modes for velocity-updating.Especially,those improved swarm optimizers using the completely controllable strategy are not only of high precision,but also of faster convergence,both of which imply their better overall performance in multimodal optimization.