混和进化算法求解具有分段恶化效应的并行机调度问题

本文提出了一种新的混合进化算法求解具有线性恶化的并行机调度问题,目标是使总完工时间最小.该算法采用对立策略以及最小比率优先规则生成初始种群,并且引入种群多样度指标加快算法的收敛;同时加入含有3-opt扰动算子的变邻域搜索算法对遗传算法得到的结果进行局部搜索.通过对不同规模算例的实验进行仿真,其结果与传统GA和VNS算法相比,效果均有所提升.

Hybrid Evolutionary Algorithm for Solving Parallel Machine Scheduling Problems with Step-Piece Deteriorating Processing Time

A new hybrid evolutionary algorithm is proposed to solve the parallel machine scheduling problems with step-piece deteriorating processing time. The goal is minimizing the total completion time. The algorithm uses opposing strategy and Smallest Rate First (SRF) rule to generate the initial population to improve its quality, and the algorithm considers the population diversity to accelerate the convergence of the algorithm, which improves the calculation efficiency of the algorithm. At the same time, a variable neighborhood search algorithm with 3-opt perturbation operator is added to improve the quality of the results obtained by the genetic algorithm. By simulating the experiments of different scale examples, the results are improved compared with the traditional GA and VNS algorithms.