【摘要】：遺傳算法是一類簡單易用、魯棒性強(qiáng)、具備并行能力且擴(kuò)展空間大的智能優(yōu)化算法。它模擬了生物進(jìn)化"優(yōu)勝劣汰、適者生存"的自然法則,而且由于遺傳算法對待優(yōu)化問題沒有連續(xù)性或可微的要求,被廣泛應(yīng)用于科研與工程領(lǐng)域。RNA遺傳算法(RNA-GA)是受RNA分子操作啟發(fā)的一類新型遺傳算法,本文在前人的研究基礎(chǔ)上,針對RNA遺傳算法的改進(jìn)以及在橋式吊車中的應(yīng)用問題進(jìn)行了較為深入的探討。本文的主要研究內(nèi)容如下:(1)簡要綜述了遺傳算法的發(fā)展歷程與研究現(xiàn)狀,以及橋式吊車的建模與控制方面的研究狀況。(2)提出了一種受魚群行為啟發(fā)的RNA遺傳算法(fsRNA-GA)。在fsRNA-GA中采用了所設(shè)計的一種鄰域搜索操作,該操作通過定義適應(yīng)度函數(shù)的擁擠度因子來衡量個體與個體之間的擁擠程度,以便在局部空間內(nèi)發(fā)現(xiàn)更有潛力的個體。針對待優(yōu)化參數(shù)較多的情況,使用矩陣編碼的方式代替RNA-GA中的鏈?zhǔn)骄幋a。通過對一些典型測試函數(shù)的尋優(yōu)實驗,表明了該算法對低、高維無約束優(yōu)化問題尋優(yōu)的良好性能。將所提出的fsRNA-GA用于優(yōu)化徑向基函數(shù)(RBF)神經(jīng)網(wǎng)絡(luò)基函數(shù)中心,利用橋式吊車實驗平臺采集的數(shù)據(jù)訓(xùn)練神經(jīng)網(wǎng)絡(luò),建立橋式吊車位置和擺角的RBF神經(jīng)網(wǎng)絡(luò)模型,仿真實驗表明了所建立神經(jīng)網(wǎng)絡(luò)模型的有效性。(3)提出了一種高位密碼子選擇操作RNA遺傳算法(csRNA-GA)。該算法通過選擇操作增加了精英基因在進(jìn)化過程中進(jìn)入下一代的概率,豐富了種群的多樣性。通過對一些典型測試函數(shù)的尋優(yōu)實驗,結(jié)果表明所提算法具有更好的全局搜索能力和平均收斂精度。針對橋式吊車系統(tǒng)PID控制器參數(shù)整定問題,將csRNA-GA算法用于優(yōu)化橋式吊車系統(tǒng)位置和擺角雙PID控制器參數(shù),仿真實驗表明優(yōu)化得到的PID控制器參數(shù)能夠?qū)崿F(xiàn)二維橋式吊車快速、無超調(diào)、擺動幅度小的控制效果。
[Abstract]:Genetic algorithm is a kind of intelligent optimization algorithm, which is simple and easy to use, strong robustness, parallel ability and large expansion space. It simulates the natural law of biological evolution, "survival of the fittest," and because genetic algorithms have no continuity or differentiability in their treatment of optimization problems, RNA genetic algorithm (RNA-GA) is a new type of genetic algorithm inspired by RNA molecular operations. The improvement of RNA genetic algorithm and its application in bridge crane are discussed in this paper. The main contents of this paper are as follows: (1) the development and research status of genetic algorithms are summarized briefly. And the research status of modeling and control of bridge crane. (2) A new RNA genetic algorithm (fsRNA-GA), which is inspired by fish herd behavior, is proposed. A neighborhood search operation designed in fsRNA-GA is used to measure the degree of crowding among individuals by defining the crowding factor of fitness function in order to find more potential individuals in local space. In view of the large number of parameters to be optimized, matrix coding is used instead of chain coding in RNA-GA. Experiments on some typical test functions show that the algorithm has good performance for low and high dimensional unconstrained optimization problems. The proposed fsRNA-GA is used to optimize the radial basis function (RBF) neural network basis function center. The neural network is trained by the data collected from the bridge crane experiment platform, and the RBF neural network model of the bridge crane position and swing angle is established. The simulation results show the effectiveness of the proposed neural network model. (3) A high-order codon selection operation RNA genetic algorithm (csRNA-GA) is proposed. This algorithm increases the probability of elite genes entering the next generation in the process of evolution, and enriches the diversity of the population. Through the optimization experiments of some typical test functions, the results show that the proposed algorithm has better global search ability and average convergence accuracy. Aiming at the parameter setting problem of PID controller in bridge crane system, the csRNA-GA algorithm is used to optimize the parameters of double PID controller of bridge crane system. The simulation results show that the optimized parameters of the PID controller can realize the control effect of the two-dimensional bridge crane with fast speed, no overshoot and small swing amplitude.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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