论文部分内容阅读
针对粒子群优化算法早熟收敛及搜索精度较低的问题,提出一种基于种群多样性的改进型粒子群优化算法(PDPSO).首先,利用种群多样性描述粒子的分布状态,获得了进化过程中粒子飞行的非线性特征;其次,基于粒子的种群多样性设计了自适应惯性权重调整策略,实现了粒子全局探索能力及局部开发能力的平衡;最后,利用标准测试函数检验PDPSO算法性能,并将PDPSO算法应用于污水处理过程能耗模型优化,实验结果表明:与标准粒子群及其他改进粒子群算法相比,PDPSO算法具有较高的搜索精度,并有效地避免粒子陷入局部最优;同时,PDPSO算法能够实现污水处理过程的优化,保证污水出水水质的前提下,降低了污水处理过程的运行能耗.“,”To overcome the premature convergence and low searching accuracy of the particle swarm optimization (PSO) algorithm,we propose a population diversity-based particle swarm optimization algorithm (PDPSO).First,we introduce the nonlinear characteristics of particles by the population diversity to describe the distribution state in the searching process.Second,we develop an adaptive inertia weight adjustment strategy to balance the global exploration ability and the local exploitation ability based on the population diversity of particles.Finally,we test the performance of PDPSO by using the standard test functions.We use the proposed PDPSO algorithm to optimize the energy consumption of the wastewater treatment process.Unlike the standard PSO algorithm and other improved PSO algorithms,the proposed PDPSO algorithm can avoid being trapped in the local optimum and achieve high accuracy,as demonstrated by simulation results.The proposed PDPSO algorithm can optimize the wastewater treatment process to reduce energy consumption within effluent water qualities.