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研究生:張鈞彥
研究生(外文):Chun-Yen Chang
論文名稱:運用全域最佳訊息與活化策略改良人工蜂群演算法
論文名稱(外文):A Novel Artificial Bee Colony Algorithm Using Information of The Global Best Solution and Activation Strategy
指導教授:李維平李維平引用關係
指導教授(外文):Wei-Ping Lee
學位類別:碩士
校院名稱:中原大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:54
中文關鍵詞:仿生群體智慧人工蜂群演算法
外文關鍵詞:Swarm intelligenceArtificial bee colony algorithmbiological-inspired
相關次數:
  • 被引用被引用:1
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  • 評分評分:
  • 下載下載:1
  • 收藏至我的研究室書目清單書目收藏:0
人工蜂群演算法是 Karaboga 在 2005 年提出的一種基於群體智慧的仿生優化演算法,為 近年來較新的熱門最佳化問題求解的演算法之一。雖然有優異的求解能力,但仍有過早收斂 和可能陷入區域解等問題的改良研究空間。
本研究提出運用最佳訊息的移動與活化等策略,以針對標準版人工蜂群(ABC)演算法的 缺點進行改良。運用最佳訊息的移動策略能加快收斂速度,活化策略則能在陷入區域解時能 提供有效跳脫的方法。
實驗結果表明,本研究提出新的運用全域最佳訊息與活化策略改良人工蜂群演算法稱作 BPABC 演算法,在單峰、多峰等數種函數問題求解效能與效率上,均優於標準版人工蜂群 (ABC)演算法。

Artificial bee colony algorithm is invented in 2005 by the Karaboga a biological-inspired optimization algorithm based on swarm intelligence, the most popular in recent years than the best one of the new algorithm for solving problems. Although better than other algorithms for solving ability, but there is still likely to fall into premature convergence and suboptimal solutions issue.
We propose the use of information of the global best solution and activation strategy to modified the original ABC algorithm to improve the drawbacks. Use of information of the global best solution strategy can speed up the convergence, the activation strategy into a regional solution is able to provide exploration capability.
In this paper. We present an algorithm using information of the global best solution and activation strategy called the BPABC algorithm. To improve and enhance the original ABC algorithm in solving ability. The experimental results show that the new BPABC in the uni-modal, multi-modal benchmark functions on problem-solving performance are better than the original ABC algorithm.

目錄
摘要 I
Abstract II
致謝詞 III
目錄 IV
圖目錄 III
表目錄 IV
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 2
1.4 研究工具 4
第二章 文獻探討 5
2.1 演化式計算 5
2.2 人工蜂群演算法 6
2.2.1 人工蜂群演算法概念 6
2.2.2 人工蜂群演算法介紹 7
2.2.3 人工蜂群演算法流程 8
2.3 相關研究 10
第三章 研究方法 15
3.1 研究設計 15
3.1.1 運用全域最佳訊息的移動 - Base Move 15
3.1.2 運用全域最佳訊息的移動 - Small E-Move 17
3.1.3 運用全域最佳訊息的移動 - Big E-Move 17
3.1.4 活化策略 18
3.2 人工蜂群演算法改良後流程 19
3.3 測試函數 22
3.4 實驗環境 25
第四章 實驗結果 26
4.1 與標準人工蜂群演算法比較 26
4.2 改良策略成效比較實驗 27
4.2.1 改良策略成效比較實驗–Sphere函數 28
4.2.2 改良策略成效比較實驗–Griewank函數 29
4.2.3 改良策略成效比較實驗–Rastrigin函數 30
4.2.4 改良策略成效比較實驗–Rosenbrock函數 31
4.2.5 改良策略成效比較實驗–Ackley函數 32
4.2.6 改良策略成效比較實驗–Schaffer函數 33
4.3 效能綜合比較分析 34
4.4 相關研究文獻比較 38
第五章 結論與建議 42
5.1 研究結論 42
5.2 未來研究方向 42
參考文獻 43

圖目錄
圖1-1 研究流程 3
圖 2-1 演化流程 6
圖 2.2 人工蜂群演算法流程示意圖 9
圖3-1 人工蜂群演算法改良方法示意圖 15
圖3-2 標準版ABC演算法工蜂移動範圍 16
圖3-3 運用全域最佳訊息後工蜂的移動範圍 16
圖3-4活化運算示意圖 19
圖3-5 ABC與BPABC流程比較圖 21
圖3-6 sphere三維圖示 22
圖3-7 Griewank三維圖示 23
圖3-8 Rastrigin三維圖示 23
圖3-9 Ronsenbrock三維圖示 24
圖3-10 Ackley三維圖示 24
圖3-11 Schaffer三維圖示 25
圖4-1 比較分析示意圖 27
圖4-2 實驗方法示意圖 27
圖4-3 Sphere的60維收斂圖 29
圖4-4 Griewank的60維收斂圖 30
圖4-5 Rastrigin的60維收斂圖 31
圖4-6 Rosenbrock的3維收斂圖 32
圖4-7 Ackley的60維收斂圖 33
圖4-8 Schaffer的3維收斂圖 34
圖4-9 Sphere效能綜合比較 35
圖4-10 Griewank效能綜合比較 35
圖4-11 Rastrigin效能綜合比較 36
圖4-12 Rosenbrock效能綜合比較 36
圖4-13 Ackley效能綜合比較 37
圖4-14 Schaffer效能綜合比較 37
圖4-15 效能總比較 38

表目錄
表2-1效能比較重要相關研究 13
表2-2重要改良相關研究 14
表3-1 實驗測試函數表 22
表3-2 實驗環境 25
表4-1 實驗參數設定 26
表4-2 Sphere函數實驗結果 28
表4-3 Griewank函數實驗結果 29
表4-4 Rastrigin函數實驗結果 30
表4-5 Rosenbock函數實驗結果 31
表4-6 Ackley函數實驗結果 32
表4-7 Schaffer函數實驗結果 33
表4-8 收斂迭代綜合比較 34
表4-9 與GABC實驗比較參數設定 39
表4-10 與GABC方法比較改良結果 40
表4-11與ABC驗證效能期刊實驗參數設定 41
表4-12與ABC驗證效能期刊比較結果 41
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