發(fā)布時間：2018-05-15 02:09

  本文選題：分布估計算法 + MIMIC算法　； 參考：《太原科技大學》2017年碩士論文

【摘要】：優(yōu)化設計已經成為一門獨立的學科,并且逐漸地滲透在各個行業(yè)中.優(yōu)化設計發(fā)展初期使用的手段是傳統(tǒng)優(yōu)化算法,隨著群智能進化算法的發(fā)展,如今,越來越多的群智能算法應用在優(yōu)化設計中,分布估計算法作為一種基于概率模型的群進化算法,有著較強的全局搜索能力,但是該算法后期容易對解空間過于依賴,使得進化較慢,本文創(chuàng)新點是對MIMIC算法進行改進,提出兩種有效的,可行的,求精能力強的算法,將改進后的算法應用在兩個簡單優(yōu)化設計實例中,體現(xiàn)出改進后的算法在實際應用中的價值,為解決優(yōu)化設計問題提供了一種新的思路和方法.本文的主要工作:在MIMIC算法進化過程中加入了局部搜索能力強的模式搜索法,提出一種結合模式搜索法的混合MIMIC算法.算法是在種群進化過程中,在當前群體里隨機選取若干點作為初始點,進行模式搜索,將得到的個體作為新群體的一部分增加種群的多樣性.利用算法對六個測試函數進行測試,通過三個性能指標,即固定進化代數內的最優(yōu)值,到達確定閾值時的進化代數和達標率驗證改進后的算法是有效的,可行的,求精能力有所改進的算法.并通過不同維數下MIMIC算法和改進后的算法結果的比較,得到維數越高,MIMIC算法和改進后的MIMIC算法的尋優(yōu)能力越低,說明函數的復雜度對算法的收斂能力有影響,但是維數越高,改進后的MIMIC算法的優(yōu)勢越明顯.在MIMIC算法種群進化過程中加入旋轉方向法,提出一種結合旋轉方向法的混合MIMIC算法.算法是在MIMIC算法選擇完優(yōu)勢群體后,在當前群體中隨機選取若干點作為初始點進行旋轉方向法搜索,將得到的個體作為新群體中的一部分,改善種群進化后期個性差異較小的不足之處.通過測試函數測試其性能,得到改進后的算法既結合了MIMIC算法全局搜索能力強的優(yōu)勢,又結合了旋轉方向法局部求精能力強的優(yōu)勢,且算法不要求目標函數必須可導,是解決目標函數不可導或者求導麻煩的一種有效的算法.將結合旋轉方向法的混合MIMIC算法應用在蝸桿傳動模型中,尋找合適的蝸桿頭數,模數,直徑系數使得蝸輪齒圈體積最小,優(yōu)化結果表明改進后的算法最優(yōu)值和進化代數小于標準MIMIC算法,將得到的結果進行圓整,并與常規(guī)優(yōu)化設計相比,體積減少了31%,說明改進后的算法在蝸桿傳動模型中是可行的.將結合模式搜索法的混合MIMIC算法應用在焊接梁模型中,這是一個最小化總費用問題,將改進后的算法的優(yōu)化結果與標準MIMIC算法的結果以及已知的兩種算法的結果相比較,改進后的算法結果明顯小于其他算法,表明改進后的算法在焊接梁優(yōu)化設計中是有效的.
[Abstract]:Optimization design has become an independent subject, and gradually infiltrated into various industries. With the development of swarm intelligence evolutionary algorithm, more and more swarm intelligence algorithms are used in optimization design. As a probabilistic model based swarm evolution algorithm, the distribution estimation algorithm has a strong global search ability. However, it is easy to rely on the solution space too much in the later stage of the algorithm, which makes the evolution slow. The innovation of this paper is to improve the MIMIC algorithm. Two effective, feasible and powerful algorithms are proposed. The improved algorithm is applied to two simple optimization design examples, which reflects the value of the improved algorithm in practical application. It provides a new way of thinking and method for solving the problem of optimal design. The main work of this paper is as follows: in the evolution of MIMIC algorithm, a new hybrid MIMIC algorithm is proposed, which has strong local search ability. In the process of population evolution, the algorithm selects a number of points randomly as initial points in the current population and carries out pattern search. The resulting individuals are regarded as part of the new population to increase the diversity of the population. Using the algorithm to test six test functions, through three performance indexes, that is, the optimal value in the fixed evolutionary algebra, the evolutionary algebra when the threshold is determined and the reaching rate to verify that the improved algorithm is effective and feasible. An improved algorithm for refinement. By comparing the results of MIMIC algorithm and improved algorithm under different dimensions, the higher the dimension is, the lower the optimization ability of MIMIC algorithm and improved MIMIC algorithm is, which indicates that the complexity of function has an effect on the convergence ability of the algorithm, but the higher the dimension is, the higher the dimension is. The advantages of the improved MIMIC algorithm are more obvious. In this paper, a hybrid MIMIC algorithm based on rotation direction is proposed by adding the rotation direction method into the evolution process of the MIMIC algorithm. After the MIMIC algorithm selects the dominant population, the algorithm selects a number of points randomly as the initial point in the current population for the rotation direction method search, and takes the individual as a part of the new population. The deficiency of improving the personality difference in the late evolutionary stage of the population. By testing its performance, the improved algorithm not only combines the advantages of global search ability of MIMIC algorithm, but also combines the advantages of local refinement ability of rotation direction method, and the algorithm does not require that the objective function must be differentiable. It is an effective algorithm to solve the problem that the objective function is nondifferentiable or derivable. The hybrid MIMIC algorithm combined with the rotation direction method is applied to the worm gear transmission model to find the appropriate worm head number, modulus and diameter coefficient to minimize the volume of the worm gear ring. The optimization results show that the optimal value and evolutionary algebra of the improved algorithm are smaller than that of the standard MIMIC algorithm. The results obtained are rounded, and compared with the conventional optimization design, the volume of the improved algorithm is reduced by 31%, which shows that the improved algorithm is feasible in the worm transmission model. The hybrid MIMIC algorithm combined with the pattern search method is applied to the welding beam model. It is a problem of minimizing the total cost. The optimization results of the improved algorithm are compared with the results of the standard MIMIC algorithm and the results of the two known algorithms. The result of the improved algorithm is obviously smaller than that of other algorithms, which shows that the improved algorithm is effective in the optimization design of welded beam.
【學位授予單位】：太原科技大學
【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：1890485