臺灣博碩士論文加值系統

本篇論文的目的是針對基本型細菌覓食演算法特性進行改良，新的細菌覓食演算法稱為改良式細菌覓食演算法(Improved bacterial foraging optimization, IBFO)。細菌覓食演算法是新的群體智慧方法，透過趨化、複製和淘汰遷徙三項操作來進行全域和區域隨機搜尋，此強大且有效的方法可用來解決複雜的現實世界最佳化問題；但是其有三個顯著缺點：參數過多造成應用上困難，翻滾方向由亂數產生而非資訊分享機制導引，和覓食步伐固定導致收斂不佳的問題。本研究將嚐試改善此三項缺點：基於減少設定參數值的基礎上提出獨立細菌概念、改良細菌吸引力，和自調適步伐值三種方法來優化解品質和搜尋能力。最後，利用七個連續最佳化的測試函數，進行四種實驗測試IBFO的績效，分別為關鍵參數實驗、群體智慧演算法比較實驗，並與其他兩種演算法進行比較實驗。從實驗結果和統計分析中可以明顯看出IBFO具有較優良的演算能力。

This paper proposes an improved approach involving bacterial foraging optimization algorithm (BFOA) behavior. The new algorithm is called improved bacterial foraging optimization (IBFO). BFOA is a new swarm intelligence technique. Three main BFOA operation are chemotaxis, reproduction and elimination-dispersal, which are applied to global and local random searches. This powerful and effective algorithm has been used to solve various real-world optimization problem. However , BFOA has several shortages: many parameters needed to be set ; tumble angles are generated randomly and a fixed chemotactic step size causing poor convergence. In this paper, we try to improve these shortages of BFOA base on reduce setting parameters. Finally, we compare the performance of IBFO with the classical BFOA, testing them on seven widely-used benchmark functions. The experimental result shows that the IBFO is very competitive and outperforms the BFOA.

誌謝 IV
摘要 V
表目錄 IX
圖目錄 X
第1章 簡介 1
1.1 研究背景和動機 1
1.2 研究目的 2
1.3 研究方法 2
1.4 論文架構 3
第2章 標準細菌覓食演算法 4
2.1 趨化操作 5
2.2 複製操作 6
2.3 遷徙淘汰操作 7
2.4 細菌吸引力 7
2.5 細菌覓食演算法參數簡介 8
第3章 文獻探討 9
3.1 細菌覓食演算法混合其他演算法之研究 9
3.2 細菌覓食演算法趨化與複製操作改良之研究 11
3.3 細菌覓食演算法參數優化之研究 13
3.4 細菌覓食演算法演化總結 13
第4章 改良式細菌覓食演算法 16
4.1 IBFO基本概念 16
4.2 獨立細菌操作 17
4.3 細菌吸引力 22
4.4 自調適步伐值機制 24
4.5 改良式細菌覓食演化流程 26
第5章 實驗和比較 29
5.1 連續最佳化的測試函數 29
5.2 關鍵參數實驗 31
5.3 群體智慧演算法比較實驗 34
5.4 ABFOA比較實驗 38
5.5 BFOLS比較實驗 43
第6章 結論 45
參考文獻 46
附錄A 連續最佳化的測試函數[20] 48
附錄B 群體智慧演算法比較實驗收斂圖 54

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