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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/88061

    Title: PSO-SA混合法於結構多目標最佳化之應用
    Other Titles: Multiobjective optimization of structure by hybrid PSO-SA method
    Authors: 黃宗鴻;Huang, Zong-Hong
    Contributors: 淡江大學航空太空工程學系碩士班
    張永康;Chang, Yeong-Kang
    Keywords: 粒子群演算法;particle swarm optimization;模擬退火法;多目標最佳化;Simulated Annealing;Multiobjective optimization
    Date: 2013
    Issue Date: 2013-04-13 11:58:59 (UTC+8)
    Abstract: 本研究應用PSO-SA混合法於結構多目標最佳化設計中,粒子群演算法是種模仿自然界鳥類覓食的現象進行問題求解之步驟,屬於仿生演算法一種,此演算法具有快速搜尋且較為簡易設定的優點並具有全域搜尋之特色。模擬退火法主要是根據統計熱力學的原理,模擬材料在進行退火的過程中,逐漸達到最低溫狀態的現象,並透過波茲曼函數判斷問題解被接受機率,如此能有助於跳脫區域最佳解往全域最佳解靠近的機會。


    The PSO-SA hybrid method was applied to multiobjective optimum design of structure in this study. Particle Swarm Optimization is to mimic the behavior of birds finding a good path to the food, which is one of the artificial biological algorithms. Thus, it has the merits of converge efficiently and programming easily. The advantage of Particle Swarm Optimization (PSO) is it global search technique. The Simulated Annealing (SA) method is based on the principle of statistical thermodynamic. That is material during the annealing process can be reached most cryogenic state phenomenon. The possibility of local optimum jump to global optimum can be determined by the probability of the Bozeman function.
    The structural optimization problem can be transformed into a mathematical function. The new design can be obtained by using the linear decreasing inertia weight to update velocity and position of particles. After the optimal solution was obtained, the strategy of SA method is initiated to determine whether this optimal solution should be neglected or not . In the Numerical examples, the study showed that the computational efficiency can be improved and the constraint is satisfied.
      A systematic program was developed by FORTRAN and APDL of ANSYS software. The optimum results of six different multiobjective examples showed that the PSO-SA hybrid methods are reasonable compared to other references.
    Appears in Collections:[航空太空工程學系暨研究所] 學位論文

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