CAJ | 학술논문

针对最小完工时间的流水车间作业调度问题,提出了一种自适应混合粒子群进化算法--AHPSO,将遗传操作有效地结合到粒子群算法中.定义了粒子相似度及粒子能量,粒子相似度阈值随迭代次数动态自适应变化,而粒子能量阈值与群体进化程度及其自身进化速度相关.此外,针对算法运行后期进化速度慢的缺点,提出了一种基于邻域的随机贪心策略进一步提高算法的性能.最后将此算法在不同规模的实例上进行了测试,并与其他几种具有代表性的算法进行了比较,实验结果表明,无论是在求解质量还是稳定性方面都优于其他几种算法,并且能够有效求解大规模车间作业问题.
침대최소완공시간적류수차간작업조도문제,제출료일충자괄응혼합입자군진화산법--AHPSO,장유전조작유효지결합도입자군산법중.정의료입자상사도급입자능량,입자상사도역치수질대차수동태자괄응변화,이입자능량역치여군체진화정도급기자신진화속도상관.차외,침대산법운행후기진화속도만적결점,제출료일충기우린역적수궤탐심책략진일보제고산법적성능.최후장차산법재불동규모적실례상진행료측시,병여기타궤충구유대표성적산법진행료비교,실험결과표명,무론시재구해질량환시은정성방면도우우기타궤충산법,병차능구유효구해대규모차간작업문제.
A hybrid self-adaptive algorithm is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan, which combined the particle swarm optimization algo-rithm and genetic operators together. The particle similarity and particle energy are defined. The threshold of particle similarity dynamically changes with iterations and the particle energy de-pends on the swarm evolving degree and the particle's evolving speed. In order to improve the proposed algorithm performance further, a neighborhood based random greedy search strategy is introduced to overcome the shortcoming of evolving slowly in the later running phase. Finally, the proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The result shows that the solution quality and the stability of the HPGA both precede the other two algorithms. It can be used to solve large scale flow shop sched-uling problem.