基于 PSO-MAs 算法的产品组合问题研究

摘要

针对多约束的产品组合问题，提出一种基于PSO的Memetic算法。该算法首先运用约束理论识别并剔除非瓶颈约束，然后基于伪效用比率设计了一个局部搜索算法，并将其加入到PSO算法的种群进化中，以增强PSO算法的局部学习能力。通过对算法在小规模和大规模算例中测试，表明该算法在小规模问题中优于许多已有算法，同时能在相对较短地时间内更有效地求解较大规模产品组合问题。因此本文提出的基于 PSO 的Memetic算法可以用来有效地求解实际中的产品组合问题。%To solve the product mix problem with multiple constraints , a memetic algorithms is proposed based on particle swarm optimization ( PSO) .Firstly, the problem is simplified by recognizing and removing the non-bottle-necks based on the Theory of Constraints ( TOC) .Secondly , a pseudo utility ratio based local search is proposed to improve the exploitation ability of PSO .Both small-scale benchmark datasets and a group of randomly genera-ted large-scale examples are used to test the proposed approach on solving the product mix problems .The compu-tational results show that the proposed approach outperform some existing approaches , such as TOC , revised TOC, Tabu Search(TS), Simulated Annealing(SA)and Genetic Algorithms(GA), and can solve the large-scale problems more effectively .Hence, the proposed approach can be accepted as a practical approach to solve the product mix problem .