求解多目标作业车间调度问题的混合变异杂草优化算法

针对多目标作业车间调度问题,提出一种混合变异杂草优化算法。该算法采用基于各子目标熵值权重的欧氏贴近度作为适应度值计算方法,引导种群向Pareto前端进化。在进化过程中,运用快速非支配排序策略构建Pareto档案,并利用进化种群中最优个体实时更新Pareto最优解集,提升算法的优化性能；同时通过引入变异算子增加种群多样性,避免算法陷入局部最优。最后,基于Benchmark算例的仿真实验,验证了该算法求解多目标作业车间调度问题的有效性。

Composite mutation invasive weed optimization algorithm for multi-objective Job Shop scheduling problem

To solve multi-objective job shop scheduling problem, a composite mutation invasive weed optimization algorithm was proposed. The method adopted a fitness calculating method of Euclid approach degree to help population move towards the Pareto front. In each generation of the evolving process, a fast non-dominated sorting approach was presented to improve the efficiency of constructing Pareto optimal solutions. The global best solution of evolutionary population was integrated into updating Pareto optimal solutions instantly to enhance performance of the optimization algorithm. The mutation operator was applied to increase population diversity to avoid the algorithm trapping into a local minimum. The experimental results of the Benchmark instances taken from literature demonstrated the effectiveness of the algorithm proposed on solving multi-objective job shop scheduling problems.