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

【摘要】：優(yōu)化設(shè)計已經(jīng)成為一門獨(dú)立的學(xué)科,并且逐漸地滲透在各個行業(yè)中.優(yōu)化設(shè)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(jìn)化算法的發(fā)展,如今,越來越多的群智能算法應(yīng)用在優(yōu)化設(shè)計中,分布估計算法作為一種基于概率模型的群進(jìn)化算法,有著較強(qiáng)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(jìn)化較慢,本文創(chuàng)新點(diǎn)是對MIMIC算法進(jìn)行改進(jìn),提出兩種有效的,可行的,求精能力強(qiáng)的算法,將改進(jìn)后的算法應(yīng)用在兩個簡單優(yōu)化設(shè)計實(shí)例中,體現(xiàn)出改進(jìn)后的算法在實(shí)際應(yīng)用中的價值,為解決優(yōu)化設(shè)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(jìn)化過程中加入了局部搜索能力強(qiáng)的模式搜索法,提出一種結(jié)合模式搜索法的混合MIMIC算法.算法是在種群進(jìn)化過程中,在當(dāng)前群體里隨機(jī)選取若干點(diǎn)作為初始點(diǎn),進(jìn)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(shù)進(jìn)行測試,通過三個性能指標(biāo),即固定進(jìn)化代數(shù)內(nèi)的最優(yōu)值,到達(dá)確定閾值時的進(jìn)化代數(shù)和達(dá)標(biāo)率驗(yàn)證改進(jìn)后的算法是有效的,可行的,求精能力有所改進(jìn)的算法.并通過不同維數(shù)下MIMIC算法和改進(jìn)后的算法結(jié)果的比較,得到維數(shù)越高,MIMIC算法和改進(jìn)后的MIMIC算法的尋優(yōu)能力越低,說明函數(shù)的復(fù)雜度對算法的收斂能力有影響,但是維數(shù)越高,改進(jìn)后的MIMIC算法的優(yōu)勢越明顯.在MIMIC算法種群進(jìn)化過程中加入旋轉(zhuǎn)方向法,提出一種結(jié)合旋轉(zhuǎn)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(dāng)前群體中隨機(jī)選取若干點(diǎn)作為初始點(diǎn)進(jìn)行旋轉(zhuǎn)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(jìn)化后期個性差異較小的不足之處.通過測試函數(shù)測試其性能,得到改進(jìn)后的算法既結(jié)合了MIMIC算法全局搜索能力強(qiáng)的優(yōu)勢,又結(jié)合了旋轉(zhuǎn)方向法局部求精能力強(qiáng)的優(yōu)勢,且算法不要求目標(biāo)函數(shù)必須可導(dǎo),是解決目標(biāo)函數(shù)不可導(dǎo)或者求導(dǎo)麻煩的一種有效的算法.將結(jié)合旋轉(zhuǎn)方向法的混合MIMIC算法應(yīng)用在蝸桿傳動模型中,尋找合適的蝸桿頭數(shù),模數(shù),直徑系數(shù)使得蝸輪齒圈體積最小,優(yōu)化結(jié)果表明改進(jìn)后的算法最優(yōu)值和進(jìn)化代數(shù)小于標(biāo)準(zhǔn)MIMIC算法,將得到的結(jié)果進(jìn)行圓整,并與常規(guī)優(yōu)化設(shè)計相比,體積減少了31%,說明改進(jìn)后的算法在蝸桿傳動模型中是可行的.將結(jié)合模式搜索法的混合MIMIC算法應(yīng)用在焊接梁模型中,這是一個最小化總費(fèi)用問題,將改進(jìn)后的算法的優(yōu)化結(jié)果與標(biāo)準(zhǔn)MIMIC算法的結(jié)果以及已知的兩種算法的結(jié)果相比較,改進(jìn)后的算法結(jié)果明顯小于其他算法,表明改進(jìn)后的算法在焊接梁優(yōu)化設(shè)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(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張丹;夏桂梅;;結(jié)合模式搜索法的混合MIMIC算法[J];太原科技大學(xué)學(xué)報;2016年06期

2 夏桂梅;張文林;張金風(fēng);;一種基于Minmax算法的混合MIMIC算法[J];寧夏大學(xué)學(xué)報(自然科學(xué)版);2016年04期

3 王遠(yuǎn)東;;圓柱蝸桿傳動的優(yōu)化設(shè)計[J];機(jī)械傳動;2016年02期

4 郭永鳳;;機(jī)械優(yōu)化設(shè)計理論方法研究[J];企業(yè)技術(shù)開發(fā);2016年02期

5 張慶彬;董文雷;馬伯元;賀媛媛;;基于分布估計算法的單路口交通信號控制[J];計算機(jī)系統(tǒng)應(yīng)用;2015年12期

6 張文林;夏桂梅;;一種結(jié)合微粒群算法的混合MIMIC算法[J];太原科技大學(xué)學(xué)報;2015年05期

7 尹雅麗;熊小峰;郭肇祿;;基于轉(zhuǎn)軸法的導(dǎo)向人工蜂群算法[J];江西理工大學(xué)學(xué)報;2015年05期

8 武洪恩;王宜寧;程玉玲;徐雯斐;;基于遺傳算法的蝸桿蝸輪優(yōu)化設(shè)計[J];機(jī)械工程師;2015年03期

9 張金風(fēng);夏桂梅;王泰;;一種基于罰函數(shù)的混合分布估計算法[J];西南民族大學(xué)學(xué)報(自然科學(xué)版);2015年01期

10 余娟;賀昱曜;馮曉華;;改進(jìn)的分布估計算法求解軟硬件劃分問題[J];計算機(jī)科學(xué);2014年09期

相關(guān)博士學(xué)位論文 前1條

1 何小娟;分布估計算法及其在生產(chǎn)調(diào)度問題中的應(yīng)用研究[D];蘭州理工大學(xué);2011年

相關(guān)碩士學(xué)位論文 前4條

1 呂云虹;基于混合禁忌分布估計算法的車輛路徑問題的研究[D];遼寧科技大學(xué);2016年

2 嚴(yán)莉娜;基于質(zhì)心的Copula分布估計算法及其在圖像去噪中的應(yīng)用[D];太原科技大學(xué);2015年

3 常城;最大熵分布估計算法及其應(yīng)用[D];太原科技大學(xué);2013年

4 盧青波;差異演化算法及其在機(jī)械優(yōu)化設(shè)計中的應(yīng)用[D];太原科技大學(xué);2008年

，

本文編號：1890485