【作者】 江善和； 王其申； 江巨浪；

【Author】 Shanhe Jiang,Qishen Wang,Julang Jiang Department of Physics and Power Engineering,Anqing Normal College,Anqing 246011,China

【机构】 安庆师范学院物理与电气工程学院；

【摘要】 针对粒子群优化算法容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,提出了一种基于速度差分变异的粒子群优化算法.首先,分析了基本粒子群优化算法易收敛局部的原因.其次,基于粒子速度与种群多样性的关系,给出了将差分进化算法直接对种群粒子速度实施差分变异,改变传统情况下的位置变异策略,使得算法更为有效摆脱局部极点束缚,保持快速收敛速度.最后,通过优化4个典型测试函数的仿真结果验证了本文所提算法在优化解的质量、全局搜索能力、收敛速度等方面的优异性能.更多还原

【Abstract】 To deal with the problem of premature local convergence,slow search speed and low convergence accuracy in the late evolutionary,this paper proposes a particle swarm optimization algorithm based on velocity differential mutation (VDMPSO).Firstly,The cause of local convergence in the basic PSO algorithm is elaborated.Secondly,strategies of direct mutation for the particle velocity rather than the traditional particle position with differential evolution algorithm based on analying the relations of the particle velocity and the population diversity is introduced to improve the ability of effectively breaking away from the local optimum.By adding the mutation operation to the basic PSO algorithm,the proposed algorithm can maintain the characteristic of fast speed.Finally,the signficant performances in quality of the optimal solutions,the global search ability and convergence speed of algorithm proposed in this paper are validated by optimizing four benchmark functions.更多还原