敏捷移動粒子群最佳化方法

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姓名

陳弘毅(Hong-Yi Chen) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

敏捷移動粒子群最佳化方法
(Yare immigration particle swarm optimization)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

敏捷遷移粒子群演算法(YIPSO)是藉由觀察於鳥群、魚群以及學生團體等群體來改善標準粒子群演算法(PSO)進而增強其收斂效率。通常一個族群中的個體常常會因為能力、興趣以及個性等等分為許多更小的團體，而這些小團體的行為通常會間接影響整個族群的表現。根據上述狀況，YIPSO選擇在標準演算法中加入兩種概念：將整個族群隨機分組成較小的族群，而每個小族群之中最好的個體將會帶領其他個體更迅速且有效率地往最佳的結果邁進。其二，再將在整個族群中表現較好的粒子當作菁英挑出，這些菁英將會比以往只有一個gbest對整個族群有著更大的影響性。在改善了標準粒子群演算法之後，考慮將其應用於一水輪機調速器之PID控制系統的參數選擇，且比較不同演算法對於其參數的選擇來讓整個系統穩定。

摘要(英)

The yare immigration particle swarm optimization (YIPSO) is an improved method of the standard particle swarm optimization by observing behaviors of the flocks of fishes, birds and students to enhancing the performance of the swarm. There are usually a few smaller groups in the flock because of the ability, interest, individuality, etc., and these groups might affect the result of the flock. Considering thess situations, two concept are added to PSO as YIPSO. The first one is dividing the flock into smaller groups randomly, therefore the best one of each smaller group will take other individuals to the optimal way. The second part is choosing not only one best as gbest but some behaving well in the flock as elitists. Thus these individuals performing well will make a greater impact than before. After improving the original particle swarm optimization, there is a water turbine governor system with PID controller which needs for parameter choosing, so we use some different particle swarm optimizations to select the parameter of the PID controller.

關鍵字(中)

★ 粒子群最佳化方法
★ 加速度係數
★ 隨機分組
★ 菁英

關鍵字(英)

★ sub-swarm
★ acceleration coefficient
★ elitist
★ particle swarm optimization

論文目次

目錄
摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 viii
第一章研究背景與論文架構 1
1-1 研究背景 1
1-2 本論文架構與流程 4
第二章最佳化方法 7
2-1 相關文獻探討 7
2-2 人工智慧技術最佳化方法 8
2-2-1基因演算法 (Genetic Algorithms, GA) 8
2-2-2 類神經網路 (Artificial Neural Network, ANN) 10
2-2-3 粒子群演算法 (Particle Swarm Optimization, PSO) 12
第三章粒子群最佳化方法 17
3-1 敏捷移動例子群最佳化方法 17
3-2 動態隨機分組粒子群演算法 18
3-3 菁英進步式粒子群演算法 22
3-4 以時變方式調整加速度因子 26
3-5 隨機分組與精英進步之整合 27
第四章測試模擬結果 29
4-1 演算法參數設定 29
4-2 測試函數相關資訊 30
4-3 測試結果 34
10維測試結果 34
30維測試結果 42
第五章粒子群演算法之相關應用 51
5-1 粒子群演算法相關應用 51
5-2 粒子群演算法應用於PID控制 51
第六章結論 60
6-1 總結 60
6-2 未來發展 61
參考文獻 62

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指導教授

莊堯棠(Yau-Tarng Juang)

審核日期

2012-6-19

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