本文選題：多目標(biāo)組合優(yōu)化 + 鏈接學(xué)習(xí)技術(shù)　； 參考：《天津理工大學(xué)》2017年碩士論文

【摘要】：多目標(biāo)優(yōu)化問題(Multi-objective Optimization Problem,MOP)的本質(zhì)是在某種約束條件下實現(xiàn)多個目標(biāo)函數(shù)的均衡,多目標(biāo)置換流水車間調(diào)度問題(Permutation Flow Shop Scheduling Problem,PFSP)是其應(yīng)用之一,因PFSP自身復(fù)雜程度高、不同目標(biāo)之間的沖突、多目標(biāo)測試數(shù)據(jù)不統(tǒng)一等使求解算法極具挑戰(zhàn)性。本文基于子群體進化算法(Sub-population Evolutionary Algorithm,SPEA)、分群、切比雪夫權(quán)重分割法(Chebyshev’s Partition Method)和鏈接學(xué)習(xí)技術(shù)(Linkage Learning technique,LLT)等提出基于鏈接學(xué)習(xí)的子群體進化算法(Sub-population Evolutionary Algorithm based on Linkage Learning,SEABLL),以求解多目標(biāo)置換流水車間調(diào)度問題,經(jīng)總結(jié)做出以下改善工作:(1)考慮子群體再接空間的分布,透過H劃分群體盡可能實現(xiàn)解空間上的均勻分布,利用切比雪夫方法調(diào)控權(quán)重,從而能夠找到更好的解。(2)在子群體進化算法中,利用以概率為核心的二元變量概率模型進行區(qū)塊挖掘和區(qū)塊競爭,構(gòu)建區(qū)塊后暫存數(shù)據(jù)庫供LLT組合人造解(artificial chromosome,AC)并注入演化過程,提高解的質(zhì)量,交叉方法同時進行,利用子群體篩選后的非支配解與優(yōu)質(zhì)支配解進行交叉,非支配解進行變異,并設(shè)置一定數(shù)量的進行交叉與變異以便找更廣泛的解以供篩選。為比較算法的性能,在Taillard標(biāo)準(zhǔn)例題測試,首先對比切比雪夫和線性權(quán)重所求的有效解的數(shù)量(number of efficient solutions,NES)和與參考集(reference set,RS)的平均距離(average distance,Dav),證明切比雪夫的優(yōu)越性。其次,為證明雙變量概率模型的有效性,設(shè)置代數(shù)100和200及其與子群體遺傳算法Ⅱ(sub-population genetic algorithmⅡ,SPGAⅡ)在例題ta010、ta020、ta050、ta060、ta080上的解的分布的對比,證明所提SEABLL分布較好。最后在ta001-ta092上39個標(biāo)準(zhǔn)測試例題中比較SEABLL與SPGAⅡ兩種算法所求非支配解數(shù)量(number of non-dominated solutions,NNDS)、證明92%的例題優(yōu)于SPGAⅡ,且規(guī)模較大的優(yōu)勢顯著。
[Abstract]:The essence of Multi-Objective Optimization problem (MOP) is to realize the equilibrium of multiple objective functions under some constraint conditions. The permutation flow shop scheduling problem (PFSP) is one of its applications, because of the complexity of PFSP itself. The conflict between different targets and the inconsistency of multi-objective test data make the algorithm very challenging. This paper is based on Sub-population Evolutionary algorithm (SPEA). Chebyshevs Partition method and Linkage Learning technique (LLT) are proposed to solve the multi-objective permutation job-shop scheduling problem. After summing up the following improvements are made: (1) considering the distribution of subpopulation reconnection space, the uniform distribution of solution space is realized through H partition, and the weight is adjusted by Chebyshev method. Thus a better solution can be found. (2) in the subpopulation evolutionary algorithm, the binary variable probabilistic model with probability as the core is used for block mining and block competition, and the post-block database is constructed for artificial chromosome AC and injected into the evolution process. In order to improve the quality of the solution, the crossover method is carried out simultaneously, and the non-dominant solution is crossed with the superior dominant solution after the selection of subpopulations, and the non-dominant solution is mutated, and a certain number of crossover and variation is set up in order to find a more extensive solution for screening. In order to compare the performance of the algorithm, in the Taillard standard example test, the superiority of Chebyshev is proved by comparing the quantity (number of efficient solutionsNess of the efficient solutions obtained by Chebyshev and the linear weights and the average distance between Chebyshev and the reference set (reference sets). Secondly, in order to prove the validity of the bivariate probabilistic model, the distribution of the solutions of algebras 100 and 200 and their solutions on the example of ta01010 / ta020 / ta050 / ta060 / ta080 is compared with that of the subpopulation genetic algorithm 鈪，

本文編號：2095731