【摘要】：量子遺傳算法是將量子算法和遺傳算法相結(jié)合起來的一種高效的智能優(yōu)化算法,除具有遺傳算法的優(yōu)點外,還具有全局尋優(yōu)能力強(qiáng)、收斂速度快、種群規(guī)模小等優(yōu)點。對于復(fù)雜優(yōu)化問題的求解,量子遺傳算法是一種有效的解決方法。但是量子遺傳算法在復(fù)雜函數(shù)優(yōu)化問題上存在迭代次數(shù)多、收斂速度慢、較易陷入局部最優(yōu)解的不足。為此本文對傳統(tǒng)的量子遺傳算法作改進(jìn),主要研究工作如下：一是提出了一種改進(jìn)的量子遺傳算法(IQGA),采用動態(tài)策略調(diào)整量子旋轉(zhuǎn)角,加快量子搜索的收斂速度；在量子旋轉(zhuǎn)策略中動態(tài)嵌入變異算子,增加種群的多樣性,并通過災(zāi)變算子使算法及時跳出局部最優(yōu)點,避免早熟收斂。二是在IQGA的基礎(chǔ)上提出了一種基于多種群的改進(jìn)量子遺傳算法(MPIQGA),使用多種群替代單種群,同時在種群初始化過程中采用小生境協(xié)同策略來均勻劃分量子位空間,使各子種群均勻分布到解空間,有利于保持種群的多樣性,各種群之間通過全局最優(yōu)個體來更新進(jìn)化目標(biāo)的形式聯(lián)系。多種群的并行搜索可以加快搜索速度,縮短迭代次數(shù)。實驗首先通過若干個復(fù)雜連續(xù)函數(shù)驗證改進(jìn)量子遺傳算法的可行性和有效性。物流配送中的貨物配裝問題屬于工程領(lǐng)域的約束優(yōu)化問題,本文利用IQGA和MPIQGA對一種多車型多貨物配裝問題模型進(jìn)行求解,其中對該模型的約束條件進(jìn)行變形,轉(zhuǎn)化成懲罰函數(shù)添加到適應(yīng)度函數(shù)里,并加入整車合并思想,能夠有效的減少所需配裝車輛的數(shù)量。實驗結(jié)果說明新算法用于解貨物配裝問題是可行的、有效的,新算法具有一定應(yīng)用價值。
[Abstract]:Quantum genetic algorithm (QGA) is an efficient intelligent optimization algorithm which combines quantum algorithm with genetic algorithm. In addition to the advantages of genetic algorithm, quantum genetic algorithm also has the advantages of strong global optimization ability, fast convergence speed and small population size. Quantum genetic algorithm (QGA) is an effective method for solving complex optimization problems. However, quantum genetic algorithm (QGA) has the disadvantages of many iterations, slow convergence rate and easy to fall into local optimal solution in complex function optimization problems. The main research work of this paper is as follows: firstly, an improved quantum genetic algorithm (IQGA),) is proposed to adjust the quantum rotation angle by dynamic strategy to speed up the convergence of quantum search. The mutation operator is dynamically embedded in the quantum rotation strategy to increase the diversity of the population, and the catastrophe operator is used to make the algorithm jump out of the local optimum in time and avoid premature convergence. Secondly, based on IQGA, an improved quantum genetic algorithm (MPIQGA),) based on multiple populations is proposed, in which multiple groups are used to replace single population, and niche coordination strategy is used to divide the qubit space evenly in the process of population initialization. The uniform distribution of each subpopulation to solution space is conducive to maintaining the diversity of the population and updating the formal association of evolutionary objectives by the globally optimal individual among the various groups. Parallel search for multiple clusters can speed up the search and shorten the number of iterations. Firstly, the feasibility and effectiveness of the improved quantum genetic algorithm are verified by several complex continuous functions. The cargo loading problem in logistics distribution belongs to the constrained optimization problem in the field of engineering. In this paper, we use IQGA and MPIQGA to solve a model of multi-model and multi-cargo loading problem, in which the constraint conditions of the model are deformed. The penalty function is added to the fitness function, and the whole vehicle merging idea is added, which can effectively reduce the number of vehicles that need to be installed. The experimental results show that the new algorithm is feasible and effective in solving the cargo loading problem, and the new algorithm has certain application value.
【學(xué)位授予單位】：廣西大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 李坤;李軍華;楊小芹;;遺傳參數(shù)協(xié)同進(jìn)化的自適應(yīng)遺傳算法[J];計算機(jī)仿真;2010年11期

2 戴勇謙;張明武;祝勝林;戴勇新;;一種新的量子遺傳算法變異機(jī)制[J];計算機(jī)仿真;2013年02期

3 曹春紅;王鵬;;動態(tài)種群劃分量子遺傳算法求解幾何約束[J];計算機(jī)科學(xué)與探索;2014年04期

4 傅德勝;張蓉;;一種改進(jìn)的量子遺傳算法研究[J];計算機(jī)仿真;2013年12期

5 王曉博;李一軍;;多車型多品種貨物配裝優(yōu)化問題的混合啟發(fā)式算法[J];運籌與管理;2011年06期

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本文編號：2419495