本文選題：改進(jìn)的多目標(biāo)快速群搜索算法 + 桁架結(jié)構(gòu)優(yōu)化��； 參考：《河北工程大學(xué)》2017年碩士論文

【摘要】：近年來,我國進(jìn)行了大規(guī)模的基礎(chǔ)設(shè)施建設(shè),建筑行業(yè)的發(fā)展得到了強(qiáng)有力的推動。經(jīng)濟(jì)全球化的深入發(fā)展,給建筑行業(yè)的發(fā)展帶來了眾多機(jī)遇,同時也面臨了巨大的挑戰(zhàn)。人們對建筑結(jié)構(gòu)的使用要求也更加嚴(yán)格。桁架結(jié)構(gòu)具有施工簡單、拆裝方便、運(yùn)輸便利等優(yōu)點(diǎn),且剛度和整體穩(wěn)定性好、抗震能力強(qiáng),因此被廣泛應(yīng)用于各種領(lǐng)域。研究桁架結(jié)構(gòu)的優(yōu)化設(shè)計具有很好的現(xiàn)實(shí)意義。傳統(tǒng)的結(jié)構(gòu)優(yōu)化設(shè)計模式為先根據(jù)經(jīng)驗(yàn)判斷,然后進(jìn)行重新假定,這種傳統(tǒng)方法效率低且需消耗大量的人力和時間,已不能滿足復(fù)雜結(jié)構(gòu)的優(yōu)化需求。人們在研究和模擬生物覓食行為的基礎(chǔ)上,提出了一種基于計算機(jī)編程軟件的群體智能優(yōu)化算法。該類算法由于其計算簡單、參數(shù)設(shè)置少等優(yōu)越性,已被廣泛應(yīng)用于機(jī)械設(shè)計、建筑結(jié)構(gòu)設(shè)計、電力系統(tǒng)優(yōu)化等領(lǐng)域。本文針對多目標(biāo)快速群搜索優(yōu)化算法(MQGSO)的不足之處進(jìn)行了改進(jìn),提出了改進(jìn)的多目標(biāo)快速群搜索優(yōu)化算法(IMQGSO),并嘗試將該算法應(yīng)用于多個桁架結(jié)構(gòu)的優(yōu)化設(shè)計中。本文對MQGSO算法改進(jìn)的幾個方面主要有:一,種群初始化時引入了混沌的思想,降低了算法初始化的隨機(jī)性,提高了算法的收斂速度;二,約束處理方面引入半可行域的概念,充分利用最優(yōu)解附近的不可行解的有價值信息,并保證算法搜索方向的正確性;三,引入比例閥值R,嚴(yán)格控制有利個體的比例,保證算法最終收斂于可行域內(nèi)。本文首次將改進(jìn)后的算法應(yīng)用于桁架結(jié)構(gòu)優(yōu)化設(shè)計中,分別對3個桁架結(jié)構(gòu)進(jìn)行了多目標(biāo)連續(xù)變量優(yōu)化設(shè)計、2個桁架結(jié)構(gòu)進(jìn)行了多目標(biāo)離散變量優(yōu)化設(shè)計,并將優(yōu)化結(jié)果與MQGSO算法及其他算法對比。結(jié)果表明,改進(jìn)的快速群搜索優(yōu)化算法收斂速度快、收斂精度高、Pareto Front分布均勻廣泛,對桁架結(jié)構(gòu)優(yōu)化效果顯著,能廣泛應(yīng)用于桁架結(jié)構(gòu)多目標(biāo)優(yōu)化設(shè)計中。
[Abstract]:In recent years, our country has carried on the large-scale infrastructure construction, the construction industry development has obtained the powerful impetus. With the development of economic globalization, it brings many opportunities and challenges to the development of construction industry. The use of building structures is also more stringent. Truss structure has the advantages of simple construction, convenient disassembly and assembly, convenient transportation, good stiffness and overall stability, strong seismic capacity, so it is widely used in various fields. It is of great practical significance to study the optimal design of truss structures. The traditional structural optimization design pattern is judged first by experience and then re-assumed. This traditional method is inefficient and requires a lot of manpower and time, so it can not meet the optimization requirements of complex structures. Based on the research and simulation of foraging behavior, a swarm intelligence optimization algorithm based on computer programming software is proposed. This kind of algorithm has been widely used in mechanical design, building structure design, power system optimization and other fields because of its advantages such as simple calculation, less parameter setting and so on. In this paper, the shortcomings of multi-objective fast group search algorithm (MQGSO) are improved, and an improved multi-objective fast group search algorithm (IMQGSOO) is proposed, and the algorithm is applied to the optimization design of multi-truss structures. In this paper, we improve the MQGSO algorithm in several aspects: first, we introduce chaos in population initialization, reduce the randomness of initialization, and improve the convergence speed of the algorithm; second, we introduce the concept of semi-feasible domain in constraint processing. It makes full use of the valuable information of the infeasible solution near the optimal solution and ensures the correctness of the search direction of the algorithm. Thirdly, the proportional threshold R is introduced to strictly control the proportion of the beneficial individuals, so as to ensure that the algorithm will converge in the feasible region. In this paper, the improved algorithm is applied to the optimization design of truss structures for the first time. The multi-objective continuous variable optimization design for three truss structures and the multi-objective discrete variable optimization design for two truss structures are carried out respectively. The optimization results are compared with MQGSO algorithm and other algorithms. The results show that the improved fast group search algorithm has the advantages of fast convergence speed, high convergence precision and uniform and wide Front distribution. It can be widely used in the multi-objective optimization design of truss structures because of its remarkable effect on the optimization of truss structures.
【學(xué)位授予單位】：河北工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TU323.4

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫厚舉;楊世達(dá);;一種基于多目標(biāo)的軟件需求選擇群智能優(yōu)化算法[J];計算機(jī)與現(xiàn)代化;2016年12期

2 李智;;智能優(yōu)化算法研究及應(yīng)用展望[J];武漢輕工大學(xué)學(xué)報;2016年04期

3 梁靖昌;李麗娟;;多目標(biāo)群智能雜交算法及雙層球面網(wǎng)殼動力優(yōu)化[J];空間結(jié)構(gòu);2016年03期

4 李艷麗;黃天民;劉雅雅;;一種新型的帶有小生境技術(shù)和精英集策略的多目標(biāo)粒子群算法[J];西華大學(xué)學(xué)報(自然科學(xué)版);2016年01期

5 于立君;陳佳;劉繁明;王輝;;改進(jìn)粒子群算法的PID神經(jīng)網(wǎng)絡(luò)解耦控制[J];智能系統(tǒng)學(xué)報;2015年05期

6 安曉偉;蘇宏升;;一種改進(jìn)的群搜索優(yōu)化算法[J];鄭州大學(xué)學(xué)報(工學(xué)版);2015年02期

7 熊湛;磨季云;;塔吊起重機(jī)臂桁架的拓?fù)鋬?yōu)化設(shè)計[J];武漢科技大學(xué)學(xué)報;2014年03期

8 許妍嫵;王春潔;宋順廣;;空間展開桁架結(jié)構(gòu)的減振優(yōu)化設(shè)計方法[J];機(jī)械設(shè)計與制造;2014年04期

9 肖菁;陳鳳蓮;湯健超;;基于蟻群算法的多目標(biāo)優(yōu)化技術(shù)研究[J];華南師范大學(xué)學(xué)報(自然科學(xué)版);2014年01期

10 金晶;李麗娟;何嘉年;劉鋒;;快速群搜索算法用于桁架結(jié)構(gòu)多目標(biāo)優(yōu)化[J];空間結(jié)構(gòu);2013年04期

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