基于小生境的自适应多目标遗传算法求解流水车间调度问题

Self-adaptive MOGA with Niche-Based for Flow Shop Scheduling

流水车间调度问题属于NP完全问题。为了更高效地求解多目标流水车间调度这一问题,提出了一种新的混合多目标遗传算法,采用小生境技术、双重精英策略及非劣解局部搜索,并且可根据适应度来自动调节交叉和变异概率。实验表明,该算法具有更快的收敛速度和优化效果。

Flow shop scheduling is proved to be a NP-complete problem. In order to solve this problem effectively, a new method based on hybrid multi-objective genetic algorithm was proposed. In this method , a Niche Genetic Algorithm, a policy of double elite and a Pareto local search strategy was used. It can automatically adiust crossover probability and mutation probability according to fitness. The results show that this algorithm has good convergence speed and effective opti- mization.