English  |  正體中文  |  简体中文  |  Items with full text/Total items : 52568/87720 (60%)
Visitors : 9374440      Online Users : 157
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94504

    Title: 應用雙演化法於結構最佳化設計之研究
    Other Titles: Optimum design of structures by dual evolution strategy
    Authors: 張維恩;Chang, Wei-En
    Contributors: 淡江大學航空太空工程學系碩士班
    張永康;Chang, Yeong-Kang
    Keywords: 雙演化演算法;粒子群演算法;差分演化演算法;最佳化設計;Dual Evolution Strategy;particle swarm optimization;Differential Evolution;Optimum Design
    Date: 2013
    Issue Date: 2014-01-23 14:43:03 (UTC+8)
    Abstract: 本論文提出結合粒子群演算法與差分演化演算法的雙演化演算法於結構最佳化設計中。粒子群演算法為仿生演算法,其特點為收斂速度快、參數設定少、搜尋範圍廣及具有記憶性。差分演化演算法為演化式演算法,其優勢在於參數設定及架構簡單、能維持母體的多樣性、高效能及高精確度等。雙演化演算法則是利用粒子群演算法與差分演化演算法兩者同時進行運算,優點在於互相補足缺點,利用差分演化演算法的多樣性使其跳脫區域最佳解,而利用粒子群演算法的記憶性使局部搜尋更加完善,利用兩者不同的搜尋方式,並將兩者演算法之最佳值做比較及分享,以獲得最佳值。本文中針對粒子群演算法提出變速因子的改良機制,藉由判斷粒子的區域最佳解與全域最佳解的距離來改變搜尋的步伐,以改善搜尋過程之收斂效率。本研究在差分演化演算法中,選取適合的突變方式可增加解的多樣性以彌補粒子群演算法之不足。
    The PSO-DE Dual Evolution Strategy was applied to optimum design of structures in this study. Particle Swarm Optimization (PSO) algorithm is a bionic technique which has fast convergence, less parametric setting and wide search range with memory. Differential Evolution (DE) algorithm is an evolutional technique that has advantages of easy to implement, little parameter tuning requirement, and also exhibits reliable, accurate and fast convergence. In this study, a Dual Evolution Strategy that includes an improved Particle Swarm Optimization algorithm and a Differential Evolution algorithm is proposed for structural optimal design. The improved Particle Swarm Optimization algorithm adopts an alternation velocity factor that changes with the particle distance between the local and the global optimal solution. In the Differential Evolution algorithm, an appropriate mutation is selected to increase domain flexibility and improve the deficiency of Particle Swarm Optimization algorithm. This Dual Evolution Strategy utilizes the domain flexibility offered by the Differential Evolution algorithm and the local search memory of the Particle Swarm Optimization algorithm. The two algorithms are computed independently , the best result is obtained and shared between the two algorithms at each iteration. Numerical analysis showed that the results obtained from the Dual Evolution Strategy are better than those obtained individually from the Particle Swarm Optimization algorithm or Differential Evolution algorithm.
    Appears in Collections:[航空太空工程學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback