PFSP问题的混和CHIO算法优化

Optimization of Hybrid CHIO Algorithm for PFSP

在冠状病毒群体免疫优化算法基础上进行了改进形成了一种求解置换流水车间调度问题的混合算法.在群体免疫进化阶段使用了动态改变扩展速率的策略平衡了算法探索能力与开发能力,在重生阶段后增加基于差分进化的交叉阶段以增强最优解的挖掘能力;采用基于最小位置值的方式实现置换流水车间调度问题解的编码与解码.以最小化最大完工时间为求解目标,在21个Reeves测试实例上进行了实验,实验结果表明了提出算法在求解置换流水车间调度问题上的有效性.

The coronavirus herd immunity optimization(CHIO)algorithm is improved to form a hybrid algorithm for the permutation flow-shop scheduling problem(PFSP).Specifically,in the stage of herd immunity evolution,the strategy of dynamically changing the expansion rate is used to balance the exploration and developemnt ability of the algorithm.After the rebirth stage,a crossover stage based on differential evolution is added to enhance the mining ability of optimal solutions.The solution to PFSP is encoded and decoded by the smallest position value to minimize the maximum completion time.The experiments on 21 Reeves test examples indicate that the proposed algorithm is effective in solving PFSP.