混沌理論混合粒子群搜尋法之結構離散尺寸最佳化設計

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戴才淇(Tsai-chi Dai) 查詢紙本館藏

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土木工程學系

論文名稱

混沌理論混合粒子群搜尋法之結構離散尺寸最佳化設計

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摘要(中)

本文主要是針對連續數計變數問題和離散設計變數問題之結構尺寸最佳化設計，提出以群體智能和混沌理論的新組合，應用在桁架與構架結構。混沌搜尋法(CSP)從許多不同的生物群體和混沌理論的靈感形成，這方法是一種多階段最佳化技巧，採用混沌理論的兩個階段，在第一階段控制粒子群搜尋法(PSO)的參數稱為(CPVPSO)，第二階段是局部搜尋(CLSPSO)，有些桁架結構利用CSP演算法與高階啟發式搜尋法的結果進行比較，展現出CSP的有效性，且和其他高階啟發式搜尋法類似，在本文中，藉由連續設計變數問題和離散設計變數問題，探討本文方法的優劣。比較算例之結果發現，在求解連續設計變數及離散設計變數之最佳化問題都有穩定表現，求解品質較佳。

摘要(英)

This article is devoted to the presentation of the optimum design with continuous and discrete variables. A new combination of swarm intelligence and chaos theory is presented for optimal design of truss structures. Here the tendency to form swarms appearing in many different organisms and chaos theory has been the source of inspiration, and the algorithm is called chaotic swarming of particles (CSP). This method is a kind of multi-phase optimization technique which employs chaos theory in two phases, in the first phase it controls the parameter values of the particle swarm optimization (CPVPSO) and the second phase is utilized for local search (CLSPSO). Some truss structures are optimized using the CSP algorithm, and the results are compared to those of the other meta-heuristic algorithms showing the effectiveness of the new method. It’s similar to other meta-heuristic algorithms. The design examples including structure design of continuous and discrete variable problems. The results show the CSP algorithm is reliable, and solution quality in the literature is comparable to oter optimal methods.

關鍵字(中)

★ 混沌理論
★ 混沌映射
★ 混沌粒子搜尋
★ 粒子群演算(搜尋)法
★ 啟發式搜尋法

關鍵字(英)

★ Chaos theory
★ Chaotic map
★ Chaotic swarming of particles
★ Particle swarm optimization
★ Meta-heuristic searching method

論文目次

摘要 I
Abstract V
表目錄 XIV
圖目錄 XIX
第一章緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.2.1 混沌理論(Chaos Theory) 4
1.2.2 粒子群演算法(Particle Swarm Optimization, PSO) 5
1.2.3 混沌粒子搜尋法(Chaotic Swarming of Particles, CSP) 5
1.3 研究方法與內容 6
第二章混沌理論結合PSO演算法 7
2.1 粒子群演算法(PSO) 7
2.1.1 PSO基本模式 7
2.1.2 慣性權重(Inertia Weight) 8
2.1.3 PSO演算法標準程序 8
2.2 Chaos結合PSO演算法(CPVPSO) 10
2.3 混沌局部搜尋法(Chaotic Local Search Algorithm, CLSPSO) 11
2.3.1 CLSPSO演算流程說明如下: 12
2.4 結合CPVPSO與CLSPSO (稱為CSP) 17
2.4.1 CSP演算法流程說明如下： 17
2.5 結構最佳化的束制條件的處理和適應函數 20
第三章 CSP演算法 22
3.1 引言 22
3.2 CSP演算法之離散設計變數問題 22
3.2.1 慣性權重(Inertia Weight)限制 22
3.2.2 第二階段混沌局部離散搜尋程序(CLSPSO2) 23
3.2.3 離散CLSPSO2搜尋程序說明如下，於圖2-6 23
第四章數值算例 26
4.1 分析流程 26
4.2 結構分析之連續設計變數問題 27
4.2.1 25桿空間桁架之連續設計變數 27
4.2.2 72桿空間桁架之連續設計變數 31
4.2.3 200桿平面桁架之連續設計變數 35
4.3 結構分析之離散設計變數問題 40
4.3.1 10桿平面桁架 41
4.3.2 25桿空間桁架 49
4.3.3 36桿空間桁架 58
4.3.4 72桿空間桁架 67
4.3.5 132桿空間(穹頂)桁架 75
4.3.6 160桿空間桁架 81
4.3.7 單跨單層平面構架 88
4.3.8 單跨雙層平面構架 96
4.3.9 單跨八層平面構架 105
4.3.10 雙跨五層平面構架 110
第五章結論與建議 114
5.1 結論與建議 114
5.2 未來研究方向 115
參考文獻 117
附錄A 25桿空間桁架細部資料及設計結果 127
A.1 設計結果 127
附錄B 72桿空間桁架細部資料及設計結果 128
B.1 設計結果 128
附錄C 200桿平面桁架細部資料及設計結果 131
C.1 細部設計資料 131
C.2 設計結果 132
附錄D 10桿空間桁架細部資料及設計結果 144
D.1 細部設計資料 144
D.2 設計結果 144
附錄E 25桿空間桁架細部資料及設計結果 146
E.1 細部設計資料 146
E.2 設計結果 147
附錄F 36桿空間桁架細部資料及設計結果 148
F.1 細部設計資料 148
F.2 設計結果 149
附錄G 72桿空間桁架細部資料及設計結果 151
G.1 細部設計資料 151
G.2 設計結果 152
附錄H 132桿空間桁架細部資料及設計結果 155
H.1 細部設計資料 155
H.2 設計結果 156
附錄I 160桿空間桁架細部資料及設計結果 164
I.1 細部設計資料 164
I.2 設計結果 166
附錄J 單跨單層平面構架細部資料及設計結果 176
J.1 細部設計資料 176
J.2 設計結果 177
附錄K 單跨雙層平面構架細部資料及設計結果 178
K.1 細部設計資料 178
K.2 設計結果 178
附錄L 單跨八層平面構架細部資料及設計結果 180
L.1 細部設計資料 180
L.2 設計結果 188
附錄M 雙跨五層平面構架細部資料及設計結果 190
M.1 細部設計資料 190
M.2 設計結果 191

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指導教授

莊德興(Der-Shin Juang)

審核日期

2014-8-25

推文