發(fā)布時(shí)間：2018-03-07 21:27

  本文選題：多目標(biāo)優(yōu)化　切入點(diǎn)：置換流水車間調(diào)度　出處：《系統(tǒng)工程理論與實(shí)踐》2017年01期 　論文類型：期刊論文

【摘要】：針對(duì)最小化最大完工時(shí)間,總流程時(shí)間及總延遲時(shí)間的多目標(biāo)置換流水車間調(diào)度問題,提出一種改進(jìn)的混沌雜草優(yōu)化算法,該算法采用基于熵值權(quán)重的灰熵關(guān)聯(lián)度適應(yīng)值分配策略,引入快速非支配排序法生成外部檔案,并將進(jìn)化種群的更新和最優(yōu)位置的混沌搜索相結(jié)合,用于維護(hù)外部檔案,提升算法的尋優(yōu)性能.通過與NSGA-Ⅱ算法進(jìn)行OR-Library典型測(cè)試算例的對(duì)比實(shí)驗(yàn),驗(yàn)證該算法的有效性.
[Abstract]:In order to minimize the maximum completion time, total process time and total delay time, an improved chaotic weed optimization algorithm is proposed for income job-shop scheduling problem. In this algorithm, the grey entropy correlation fitness allocation strategy based on entropy weight is adopted, and the fast non-dominated sorting method is introduced to generate external files, and the update of evolutionary population is combined with the chaotic search of optimal location to maintain the external files. To improve the performance of the algorithm, the effectiveness of the algorithm is verified by the comparison of OR-Library typical test cases with NSGA- 鈪，

本文編號(hào)：1581022