【摘要】：在工程應(yīng)用和科學(xué)研究領(lǐng)域中,存在許多比較復(fù)雜的優(yōu)化問題。由于其復(fù)雜性、動態(tài)性和建模困難等一系列的問題,傳統(tǒng)的運(yùn)籌學(xué)方法已經(jīng)無法很好的解決這類優(yōu)化問題。基于啟發(fā)式的智能算法對處理這類問題表現(xiàn)出了一定的優(yōu)越性,其中人工免疫系統(tǒng)是通過模仿生物免疫系統(tǒng)的信息處理機(jī)制而發(fā)展的一種新興智能系統(tǒng),提供了噪聲忍耐、無監(jiān)督學(xué)習(xí)、自組織和記憶等進(jìn)化學(xué)習(xí)機(jī)理,為解決這類復(fù)雜優(yōu)化問題提供了新穎的方法和思路。因此,免疫算法受到各個領(lǐng)域?qū)W者們的廣泛關(guān)注。本文主要從優(yōu)化問題的角度去研究免疫算法。首先介紹了免疫算法研究現(xiàn)狀、生物學(xué)基礎(chǔ)理論及其算法原理以及柔性車間調(diào)度現(xiàn)狀及其問題描述。接著對多目標(biāo)優(yōu)化問題進(jìn)行分析,提出改進(jìn)的多目標(biāo)免疫算法,然后在21個測試問題上驗(yàn)證其性能。之后根據(jù)柔性車間調(diào)度問題特性將提出的算法進(jìn)行改進(jìn),并將其應(yīng)用到柔性車間調(diào)度問題上,進(jìn)一步驗(yàn)證了提出的算法在實(shí)際應(yīng)用中也有較好的性能。本文的主要工作如下:(1)在解決多目標(biāo)優(yōu)化問題上,本文在多目標(biāo)免疫算法的基礎(chǔ)上研究分析,提出了動態(tài)種群策略免疫算法(MOIA-DPS)。該算法主要創(chuàng)新點(diǎn)在于提出了動態(tài)種群策略(DPS),通過外部存檔狀態(tài)控制種群的大小,從而合理地利用計(jì)算資源、避免早熟收斂并且增加種群的多樣性。另外,設(shè)計(jì)了一個雙模式差分算子(TDE),結(jié)合了rand/2/bin和rand/1/bin的優(yōu)勢,提高了算法的魯棒性。之后在21個測試問題上進(jìn)行仿真實(shí)驗(yàn),與5個經(jīng)典算法以及近年來新提出的5個免疫算法進(jìn)行實(shí)驗(yàn)對比,以及驗(yàn)證提出算子DPS和TDE的有效性,實(shí)驗(yàn)結(jié)果表明提出的算法MOIA-DPS在多目標(biāo)優(yōu)化問題上具有明顯的優(yōu)勢。(2)在解決柔性車間調(diào)度問題上,本文提出了一個動態(tài)克隆種群策略免疫算法(DCPS-MOIA)。DCPS-MOIA提出了一個動態(tài)克隆種群策略,當(dāng)種群的整體提升率小于設(shè)定的值時,增大克隆種群的數(shù)量以增加基因的多樣性,從而平衡了多樣性和收斂性。并且運(yùn)用第三章提出的TDE進(jìn)行變異,提高算法的局部搜索能力和增加種群多樣性。之后分別在3個問題實(shí)例上測試算法的有效性。
[Abstract]:In the field of engineering application and scientific research, there are many complex optimization problems. Because of its complexity, dynamic and modeling difficulties, the traditional operational research method can not solve this kind of optimization problem well. The intelligent algorithm based on heuristic has some advantages in dealing with this kind of problem. The artificial immune system is a new intelligent system developed by imitating the information processing mechanism of the biological immune system, which provides noise tolerance. Evolutionary learning mechanisms such as unsupervised learning, self-organization and memory provide novel methods and ideas for solving such complex optimization problems. Therefore, the immune algorithm is widely concerned by scholars in various fields. In this paper, immune algorithm is studied from the point of view of optimization problem. Firstly, the current situation of immune algorithm, the basic theory of biology and its algorithm principle, the current situation of flexible job shop scheduling and its problem description are introduced. Then the multi-objective optimization problem is analyzed, and an improved multi-objective immune algorithm is proposed, and then its performance is verified on 21 test problems. Then the proposed algorithm is improved according to the characteristics of the flexible job shop scheduling problem and applied to the flexible job shop scheduling problem. It is further verified that the proposed algorithm has good performance in practical application. The main work of this paper is as follows: (1) on the basis of multi-objective immune algorithm, a dynamic population strategy immune algorithm (MOIA-DPS) is proposed. The main innovation of the algorithm is that the dynamic population strategy, (DPS), is proposed to control the population size through the external archival state, so as to make rational use of computational resources, avoid premature convergence and increase the diversity of the population. In addition, a double mode differential operator (TDE), is designed to improve the robustness of the algorithm by combining the advantages of rand/2/bin and rand/1/bin. Then the simulation experiments are carried out on 21 test problems, compared with 5 classical algorithms and 5 new immune algorithms proposed in recent years, and the validity of the proposed operators DPS and TDE is verified. Experimental results show that the proposed algorithm MOIA-DPS has obvious advantages in multi-objective optimization problems. (2) in order to solve the flexible job-shop scheduling problem, a dynamic clonal population strategy immune algorithm (DCPS-MOIA) is proposed in this paper. DCPS-MOIA proposes a dynamic clonal population strategy. When the overall lifting rate of the population is less than the set value, increasing the number of cloned populations to increase the diversity of genes, thus balancing diversity and convergence. The TDE proposed in Chapter 3 is used to improve the local search ability and population diversity of the algorithm. Then the effectiveness of the algorithm is tested on three problem examples.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TB497

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