本文關(guān)鍵詞：基于改進(jìn)螢火蟲算法的橋式起重機(jī)主梁優(yōu)化方法研究　出處：《中北大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 橋式起重機(jī) 螢火蟲算法 模擬退火 覓食行為 優(yōu)化設(shè)計(jì)

【摘要】：橋式起重機(jī)是制造業(yè)中必不可少的起重設(shè)備,可以提高運(yùn)輸效率,減輕勞動(dòng)強(qiáng)度。本文針對(duì)橋式起重機(jī)傳統(tǒng)優(yōu)化設(shè)計(jì)方法減重效果差、運(yùn)算效率低、無法求解復(fù)雜問題等缺點(diǎn),嘗試?yán)梦灮鹣x優(yōu)化算法(Glowworm Swarm Optimization,GSO)對(duì)橋式起重機(jī)的主梁截面尺寸進(jìn)行優(yōu)化,在滿足生產(chǎn)要求的前提下,使其質(zhì)量達(dá)到最輕。本文主要的研究?jī)?nèi)容有:(1)對(duì)國內(nèi)外起重機(jī)和螢火蟲算法的研究現(xiàn)狀進(jìn)行了分析,并對(duì)算法中存在的收斂速度慢、易陷入局部最優(yōu)等缺點(diǎn)進(jìn)行分析,提出了改進(jìn)螢火蟲優(yōu)化算法(Improvement of Glowworm Swarm Optimization,IGSO),并將其應(yīng)用于橋式起重機(jī)主梁尺寸優(yōu)化當(dāng)中。(2)研究了螢火蟲算法優(yōu)化過程中的關(guān)鍵步驟,并將覓食行為策略和自適應(yīng)慣性權(quán)重引入到基本螢火蟲算法中,最后將改進(jìn)的算法與模擬退火算法重新融合構(gòu)造,形成了本文的改進(jìn)螢火蟲算法,并用兩個(gè)典型的函數(shù)對(duì)改進(jìn)算法進(jìn)行測(cè)試與改進(jìn)前的優(yōu)化數(shù)據(jù)進(jìn)行對(duì)比,驗(yàn)證了本文算法性能和優(yōu)化結(jié)果的合理性和可行性。(3)通過對(duì)模型的分析確定了優(yōu)化流程,進(jìn)一步研究了主梁結(jié)構(gòu)和載荷分布的特點(diǎn)。最后,選取設(shè)計(jì)變量,建立目標(biāo)函數(shù),以及確定約束條件包括強(qiáng)度、剛度、穩(wěn)定性、邊界尺寸等等。在此基礎(chǔ)上建立了相應(yīng)的主梁截面尺寸優(yōu)化設(shè)計(jì)的數(shù)學(xué)模型。(4)將某型號(hào)橋式起重機(jī)的箱形主梁作為研究對(duì)象,并對(duì)算法中的主要參數(shù)進(jìn)行多次試驗(yàn),確定最適合改進(jìn)螢火蟲算法的參數(shù)。用最優(yōu)的一組控制參數(shù)來優(yōu)化主梁截面面積。優(yōu)化結(jié)果表明改進(jìn)螢火蟲算法優(yōu)化結(jié)果比初始主梁的截面面積減少了20.86%,最后,用ANSYS Workbench對(duì)優(yōu)化前后的模型進(jìn)行對(duì)比,從強(qiáng)度、剛度方面驗(yàn)證了優(yōu)化結(jié)果滿足實(shí)際工程需求。
[Abstract]:Bridge crane is an indispensable lifting equipment in manufacturing industry, which can improve transportation efficiency and reduce labor intensity. We try to use the glowworm Swarm Optimization to solve the complex problem. GSO) optimizes the cross section size of the main girder of the bridge crane under the premise of satisfying the production requirements. The main research content of this paper is: 1) the research status of crane and firefly algorithm at home and abroad is analyzed, and the convergence speed of the algorithm is slow. It is easy to fall into local optimum and other shortcomings to be analyzed. The improvement of Glowworm Swarm optimization algorithm (IGSO) is proposed. The key steps in the optimization process of the firefly algorithm are studied, and the foraging behavior strategy and adaptive inertia weight are introduced into the basic firefly algorithm. Finally, the improved algorithm and simulated annealing algorithm are recombined to form the improved firefly algorithm, and two typical functions are used to test the improved algorithm and compare the optimized data before the improvement. Verify the rationality and feasibility of the algorithm performance and optimization results. (3) through the analysis of the model to determine the optimization process, and further study the characteristics of the main beam structure and load distribution. Finally. Select the design variables, establish the objective function, and determine the constraints including strength, stiffness, stability. On the basis of this, the mathematical model of the optimization design of the cross section size of the main girder is established. The box girder of a certain type of bridge crane is taken as the research object. The main parameters of the algorithm are tested many times. The optimum parameters of the improved firefly algorithm are determined. The optimum control parameters are used to optimize the cross-section area of the main beam. The optimization results show that the optimized result of the improved firefly algorithm is 20.8 less than that of the initial main beam. 6%. Finally, the model before and after optimization is compared with ANSYS Workbench, and the results of optimization are verified from the aspects of strength and stiffness to meet the actual engineering requirements.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TH215

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