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

  更多相關文章： 橋式起重機 螢火蟲算法 模擬退火 覓食行為 優(yōu)化設計

【摘要】：橋式起重機是制造業(yè)中必不可少的起重設備,可以提高運輸效率,減輕勞動強度。本文針對橋式起重機傳統(tǒng)優(yōu)化設計方法減重效果差、運算效率低、無法求解復雜問題等缺點,嘗試利用螢火蟲優(yōu)化算法(Glowworm Swarm Optimization,GSO)對橋式起重機的主梁截面尺寸進行優(yōu)化,在滿足生產要求的前提下,使其質量達到最輕。本文主要的研究內容有:(1)對國內外起重機和螢火蟲算法的研究現(xiàn)狀進行了分析,并對算法中存在的收斂速度慢、易陷入局部最優(yōu)等缺點進行分析,提出了改進螢火蟲優(yōu)化算法(Improvement of Glowworm Swarm Optimization,IGSO),并將其應用于橋式起重機主梁尺寸優(yōu)化當中。(2)研究了螢火蟲算法優(yōu)化過程中的關鍵步驟,并將覓食行為策略和自適應慣性權重引入到基本螢火蟲算法中,最后將改進的算法與模擬退火算法重新融合構造,形成了本文的改進螢火蟲算法,并用兩個典型的函數(shù)對改進算法進行測試與改進前的優(yōu)化數(shù)據(jù)進行對比,驗證了本文算法性能和優(yōu)化結果的合理性和可行性。(3)通過對模型的分析確定了優(yōu)化流程,進一步研究了主梁結構和載荷分布的特點。最后,選取設計變量,建立目標函數(shù),以及確定約束條件包括強度、剛度、穩(wěn)定性、邊界尺寸等等。在此基礎上建立了相應的主梁截面尺寸優(yōu)化設計的數(shù)學模型。(4)將某型號橋式起重機的箱形主梁作為研究對象,并對算法中的主要參數(shù)進行多次試驗,確定最適合改進螢火蟲算法的參數(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)化結果表明改進螢火蟲算法優(yōu)化結果比初始主梁的截面面積減少了20.86%,最后,用ANSYS Workbench對優(yōu)化前后的模型進行對比,從強度、剛度方面驗證了優(yōu)化結果滿足實際工程需求。
[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.
【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TH215

【參考文獻】

相關期刊論文 前10條

1 凌波;韓子淵;王璽;;基于C#平臺的橋式起重機主梁優(yōu)化軟件開發(fā)[J];機械工程與自動化;2016年06期

2 張麗紅;余世明;;求解置換流水線調度問題的改進螢火蟲優(yōu)化算法[J];計算機科學;2016年08期

3 陳海東;莊平;夏建礦;代文章;逯洋;高奇;陳濤;;基于改進螢火蟲算法的分布式電源優(yōu)化配置[J];電力系統(tǒng)保護與控制;2016年01期

4 張丹;俞齊鑫;;橋式起重機箱型主梁的改進遺傳算法優(yōu)化設計[J];機械與電子;2015年09期

5 王蕾;;基于蛙跳算法的人工螢火蟲群優(yōu)化算法[J];信息系統(tǒng)工程;2015年07期

6 張海梁;孫婉勝;;基于螢火蟲算法的配電網(wǎng)狀態(tài)估計研究[J];電器與能效管理技術;2015年13期

7 程春英;;螢火蟲算法的研究進展[J];電子測試;2015年13期

8 唐少虎;劉小明;;一種改進的自適應步長的人工螢火蟲算法[J];智能系統(tǒng)學報;2015年03期

9 宋金云;;汽車起重機行業(yè)市場分析與未來展望[J];建設機械技術與管理;2015年01期

10 焦洪宇;周奇才;吳青龍;李文軍;李英;;橋式起重機箱型主梁周期性拓撲優(yōu)化設計[J];機械工程學報;2014年23期

，

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