改进粒子群算法在柔性作业加工时间问题研究

为了减少柔性作业加工时长，在柔性作业加工问题中，提出一种改进粒子群算法(β-PSO)。该算法以最小加工时间为目标函数，惯性权重幂函数自适应调节，随机数采用贝塔分布进行改进，选取Kacem算例进行验证，通过对比β-PSO算法与标准粒子群算法(PSO)、余弦惯性权重改进粒子群算法(CPSO)的优化结果，β-PSO算法加工时间均较低。实验结果表明，β-PSO算法在减少柔性作业加工时间问题上的有效性。

Research on Processing Time Problem of Improved Particle Swarm Optimization in Flexible Job

In order to reduce the processing time of the flexible job , an improved particle swarm algorithm( β-PSO ) is proposed in the study of the flexible job processing problem , the minimum processing time is designed as the algorithm objective function , the inertia weight power function is adaptively adjusted , the random number is improved by beta distribution.The Kacem example is selected for verification. By comparing the optimization results of β-PSO algorithm with standard particle swarm optimization( PSO ) and cosine inertia weight improved particle swarm optimization( CPSO ), the processing time of β-PSO algorithm is lower.The experimental results verify the effectiveness of the β-PSO algorithm in reducing the processing time of flexible jobs.