基于自适应认知域的粒子群性能改进方法

An Improved PSO Method Based on Adaptive Cognitive Domain

为提高粒子群算法的收敛性能,提出一种自适应粒子认知域方法.在粒子位置的更新方法中,粒子运动到当前的最好位置由计算得到的最好位置为中心,粒子的认知方向为导向来确定.利用线性惯性下降权重来实现粒子的优化.为验证该方法的有效性,将此方法应用于3种不同的粒子群方法,分别是固定权重粒子群方法、线性下降权重粒子群方法及阶梯形群体粒子群算法.实验结果表明此方法是较有效的.

To improve the convergent performance of particle swarm optimization（ PSO）, an adaptive cognitive domain particle swarm optimization （ACDPSO） method is proposed. In the updating equations of particles, the current best position, which the particle achieves, is determined by the center of the best calculated position and the cognizant direction of the particle. Linear decreasing inertia weight is used to optimize particles. Three different PSOs, particle swarm with constant weight （CWPSO）, linear decreasing inertia weight PSO （LDWPSO） and Ladder PSO （LPSO）, are combined with the proposed method to test the performance of the proposed method, and the results indicate that the proposed method is effective.