近年來有許多種類之最佳化方法，其中較為知名的有基因演算法(Genetic Algorithm, GA)、粒子群最佳化(Particle Swarm Optimization, PSO)及蟻群最佳化演算法(Ant Colony Optimization, ACO)、差異進化演算法(Differential Evolution Algorithm, DEA)，這些演算法都是觀察自然界生物活動之習性，利用電腦模擬而成之最佳化演算法；而近年來有研究學者觀察人類歷史中帝國與殖民地之資源競爭現象，並加以實作而成帝國主義競爭演算法(Imperialist Competitive Algorithm, ICA)，其效能表現也相當優越；而本研究主要為改良帝國主義競爭演算法殖民地之移動方式之缺陷，經過實驗測試後，改良後之帝國主義競爭演算法能夠獲得進一步的效能提升。

Many nature-inspired optimization methods, such as Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Differential Evolution Algorithm have received much attention for the past few decades. These algorithms are based on computer simulation of biological activity. Recently, a new nature-inspired optimization method, called Imperialist Competition Algorithm (ICA), was proposed. ICA is based on the resource competition among colonial empires. This study modifies how colonies move in ICA to reduce the chance of falling into local optimum. Our experimental results show that the proposed method outperforms ICA.

目 錄
書名頁............................................... I
論文口試委員審定書..................................... II
授權書.............................................. III
中文摘要................................................ IV
英文摘要............................................ V
誌謝................................................ VI
目 錄........................................... VII
表 目 錄.......................................... IX
圖 目 錄.......................................... XI
第一章 緒論........................................ 1
1.1 研究背景........................................ 1
1.2 研究目的........................................ 2
1.3 論文架構........................................ 2
第二章 文獻探討..................................... 4
2.1 基因演算法...................................... 4
2.2 粒子群最佳化演算法................................ 9
2.3 差異進化演算法.................................. 14
2.4 帝國主義競爭演算法............................... 16
2.4.1 同化作用..................................... 18
2.4.2 競爭作用..................................... 20
2.4.3 ICA之應用範疇.................................. 21
第三章 研究方法.................................... 24
3.1 改良式帝國主義競爭演算法.......................... 24
3.2 帝國主義競爭演算法與差異進化演算法之結合............. 31
第四章 實驗結果.................................... 34
4.1 實驗目標函式.................................... 34
4.2 實驗參數設置.................................... 35
4.3 實驗設備....................................... 35
4.4 改良式ICA與DEA結合ICA實驗結果.................... 36
4.5 綜合實驗結果比較................................ 42
第五章 結論與未來展望............................... 45
參考文獻............................................ 48

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