Analysis of Adaptive Multi Swarm Particle Swarm Optimization Algorithm and Its Applications
Since the standard particle swarm optimization(PSO)algorithm is easy to fall into local optima and its global search ability is poor.In this paper,the entire particle swarm is divided into three subgroups according to a certain percentage.And depend-ing on the nature of particles in each subgroup,particles are updated according to the different evolve strategies.This ensure the di-versity of the population and the balance of global and local search ability of the algorithm.In order to balance local and global search capabilities,learning factors are updated adaptively.And levy flight and chaotic searching is introduced.New algorithms on four test functions are compared with the other three kinds of algorithms,the results show that the new algorithm has better perfor-mance.Algorithm has also been used to solve the overdetermined linear equations,the results obtained show that the algorithm's so-lutions are very ideal.
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