摘要
An improved particle swarm optimization (PSO) algorithm based on ensemble technique is presented. The algorithm combines some previous best positions (pbest) of the particles to get an ensemble position (Epbest), which is used to replace the global best position (gbest). It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.
An improved particle swarm optimization （PSO） algorithm based on ensemble technique is presented. The algorithm combines some previous best positions （pbest） of the particles to get an ensemble position （Epbest）, which is used to replace the global best position （gbest）. It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved＇PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.
关键词
机器学习
进化计算
粒子群优化算法
系综技术
particle swarm optimization
ensemble technique
evolutionary computation
machine learning
