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  本文選題：協(xié)同進化 + 多目標優(yōu)化�。� 參考：《南京郵電大學》2017年碩士論文

【摘要】：工程實踐與科學研究中會經(jīng)常遇到一些多目標優(yōu)化問題,這些優(yōu)化問題如果采用傳統(tǒng)的解決方法處理會出現(xiàn)一定的缺陷和弊端。雖然使用進化算法解決多目標優(yōu)化問題已被證明是一個有效的方法,但是目前進化多目標優(yōu)化算法還不完善,這些進化算法還存在解集分布不均勻、收斂早熟和精度差等缺點。協(xié)同進化算法是近年來提出的一種解決多目標優(yōu)化問題的新方法,與傳統(tǒng)進化多目標優(yōu)化算法相比,它能在一定的程度上提高全局收斂性和避免早熟。然而目前的協(xié)同進化多目標優(yōu)化算法在解決多目標問題,然而目前的協(xié)同進化多目標優(yōu)化算法在解決多目標問題,得出非支配解集的分布性和多樣性方面不太理想,并且算法的全局收斂性還有待進一步提高。本文針對目前協(xié)同進化算法的不足提出了相應的改進措施和策略,整合改進的協(xié)同進化算法和進化多目標優(yōu)化機制,研究出更有效的協(xié)同進化多目標優(yōu)化算法,并將該算法有效地應用于解決機器人多目標路徑規(guī)劃問題,主要研究工作如下:(1)針對協(xié)同進化算法中選取代表個體的導向性不強的問題,提出了一種分組排序評估策略的合作協(xié)同進化算法。通過不斷將每一代新種群進行有序排列的分組評估,選擇當代最優(yōu)個體組成代表組合,使選取的代表組合更具有導向性。將該算法與其它進化算法采用典型的測試函數(shù)進行對比測試實驗,結(jié)果表明改進的協(xié)同進化算法具有更快的收斂速度和更強的全局收斂能力。將上述提出的基于分組排序評估策略的合作協(xié)同進化算法應用于復雜PID控制系統(tǒng)參數(shù)優(yōu)化,實驗結(jié)果表明,該算法能高效地搜索到給定性能指標要求的PID參數(shù)最優(yōu)組合,具有較好的應用前景。(2)針對多目標優(yōu)化算法中非支配解空間分布不均勻以及算法收斂精度不高的問題,將多種群協(xié)作的思想、快速非支配排序的方法以及精英外部檔案的策略相結(jié)合,提出了一種多種群合作協(xié)同多目標優(yōu)化算法。采用標準的多目標優(yōu)化問題測試函數(shù)集對所提出的算法與NSGA-II算法進行對比測試實驗,結(jié)果表明,所提出的多種群合作協(xié)同多目標優(yōu)化算法,能獲得更均勻和更精確的非支配解集,達到更優(yōu)的Pareto前沿面。(3)研究了應用所提出的多種群合作協(xié)同多目標優(yōu)化算法解決機器人多目標路徑規(guī)劃問題的方法及其實現(xiàn)。將機器人多目標路徑規(guī)劃任務進行建模,提出了其包含多項性能指標要求的多目標優(yōu)化模型;給出了多種群合作協(xié)同多目標優(yōu)化算法求解的實現(xiàn)方法。仿真實驗結(jié)果表明,所提出的方法能有效地獲得多目標要求下的優(yōu)化路徑。
[Abstract]:Some multi-objective optimization problems are often encountered in engineering practice and scientific research. If traditional methods are adopted to solve these optimization problems, there will be some defects and drawbacks. Although using evolutionary algorithm to solve multi-objective optimization problem has been proved to be an effective method, but at present evolutionary multi-objective optimization algorithm is not perfect, these evolutionary algorithms still have some shortcomings, such as uneven distribution of solution set, premature convergence and poor precision. Coevolutionary algorithm is a new method to solve the multi-objective optimization problem proposed in recent years. Compared with the traditional evolutionary multi-objective optimization algorithm, it can improve the global convergence and avoid prematurity to a certain extent. However, the current coevolutionary multi-objective optimization algorithm is not ideal in solving the multi-objective problem and obtaining the distribution and diversity of the non-dominated solution set. And the global convergence of the algorithm needs to be further improved. In this paper, the corresponding improvement measures and strategies are put forward to overcome the shortcomings of the current co-evolution algorithm, which integrates the improved co-evolution algorithm and the evolutionary multi-objective optimization mechanism, and develops a more effective co-evolution multi-objective optimization algorithm. The algorithm is effectively applied to solve the multi-objective path planning problem of robot. The main research work is as follows: (1) aiming at the problem that the selection of representative individual is not strong in the co-evolutionary algorithm, the main research work is as follows: A cooperative coevolutionary algorithm for grouping ranking evaluation strategy is proposed. Through the grouping evaluation of each generation of new population in order, the representative combination of the best individual is selected, which makes the representative combination more oriented. The experimental results show that the improved co-evolutionary algorithm has faster convergence speed and stronger global convergence ability than other evolutionary algorithms. The cooperative coevolutionary algorithm based on grouping ranking evaluation strategy is applied to the parameter optimization of complex pid control system. The experimental results show that the algorithm can efficiently search the optimal combination of pid parameters required by given performance index. It has a good application prospect. (2) aiming at the problems of non-dominated solution spatial distribution and low convergence precision of multi-objective optimization algorithm, the idea of multi-group cooperation is put forward. A multi-group cooperative and multi-objective optimization algorithm is proposed based on the combination of the fast non-dominated sorting method and the strategy of elite external files. The standard multi-objective optimization problem test function set is used to compare the proposed algorithm with NSGA-II algorithm. The results show that the multi-group cooperative multi-objective optimization algorithm is proposed. A more uniform and accurate set of non-dominated solutions is obtained to achieve a more optimal Pareto frontier. The method and implementation of multi-group cooperative multi-objective optimization algorithm for robot multi-objective path planning are studied in this paper. The multi-objective path planning task of robot is modeled, and the multi-objective optimization model with multiple performance requirements is proposed, and the implementation method of multi-group cooperative multi-objective optimization algorithm is presented. The simulation results show that the proposed method can effectively obtain the optimal path under multi-objective requirements.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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本文編號：2014091