Study on the Relationship Between Population Diversity and Learning Parameters in Particle Swarm Optimization
Chuanhua Zeng · Dianzi xuebao · 2011
PSO can easily suffer from the premature convergence when solving complex multimodal problems.It is an important method for relieving the premature convergence to control the population diversity by adjusting the inertia weight and acceleration coefficients.However,the setting of the inertia weight and acceleration coefficients is dependent on the design of experiments and lack of the support of the theory.To solve this problem,the new method is proposed in which the change of the prospective population diversity is used to adjust the setting of learning parameters in this paper.Firstly,the expression of the population diversity at the next time step is computed in the condition of the known current population state.Then the mathematic relationship between the inertia weight,acceleration coefficients and the population diversity at the next time step is presented by the extremum theory on multivariate function,which provides a theoretical foundation to control the population diversity by learning parameters.