基于小生境粒子群算法的柔性作业车间调度优化方法

Optimization method of flexible job-shop scheduling problem based on niching and particle swarm optimization algorithms

针对柔性作业车间调度中的多目标优化问题,提出一种将小生境技术和粒子群算法相结合求最优解的优化方法。构建了满足约束条件的多目标优化模型,采用分段排列编码的方式表示染色体,利用粒子群算法获得存储非劣解的外部存档,基于小生境技术计算粒子的删除概率对其进行更新,保证了解的精度和多样性。为从Pareto最优解集中选出一个最满意解,提出一种总体价值估计选取方法。通过试验验证了该方法的有效性。

Aiming at the multi-objective optimization problem for flexible job-shop scheduling,an optimization method based on niching particle swarm optimization algorithm was proposed to obtain an optimum solution.A multi-objective optimization model that satisfied the constraints was constructed,and the segmented arrangement code was adopted to express the chromosomes.The PSO algorithm was used to obtain the external archive that stored some non-inferior solutions,which was updated based on particles＇deletion probability to maintain the accuracy and diversity of the solutions.To select the best solution from the Pareto optimal solution set,an overall value estimation method was proposed.Through experimental verification,the method could solve multi-objective flexible job-shop scheduling problem effectively.