基于指数衰减惯性权重的分裂粒子群优化算法

Disruption particle swarm optimization algorithm based on exponential decay weight

针对粒子群优化算法因种群多样性丧失而陷入局部最优、早熟收敛的问题,提出一种基于指数衰减惯性权重的分裂粒子群优化算法(EDW-DPSO)。首先,采用半均匀初始化种群,使种群以整体均匀、局部随机的方式分布;其次,引入动态分裂算子,对满足分裂条件的粒子执行分裂操作,增加种群多样性,避免粒子陷入局部最优;最后,采用指数衰减的惯性权重,平衡粒子全局搜索和局部开发能力。实验结果表明,该算法在前期有较大的搜索空间,种群多样性增加,后期则强调局部开发,提高收敛精度和优化能力,加快粒子跳脱局部极值逼近全局最优。

To overcome the local optimum and premature convergence due to loss of population diversity of the particle swarm optimization algorithm,this paper proposed a disruption particle swarm optimization algorithm based on exponential decay weight(EDW-DPSO).Firstly,it semi-uniformly initialized the population to distribute the population in an overall uniform,locally random manner.Secondly,it introduced the dynamic splitting operator to perform splitting operations on particles which satisfying the splitting condition,increasing the diversity of the population and avoiding the particles falling into local optimum.Finally,it used the exponential decreasing inertia weight to balance the global search and local development ability of the particles.The experimental results show that the algorithm has a large search space in the early stage,and the population diversity increases.In the later stage,emphasizing the local development to improve the convergence precision and optimization ability,it can also accelerate particles jumping out of the local extremum and approximate globlal optimum.