啟發式演算法主要的概念是提供一個簡單且有效的方式來找尋近似解，這項方式使得啟發式演算法被廣泛的運用於組合最佳化的問題上。當使用者需要在有限的時間內得到一定程度的結果時，就需要此種技術。對當前的各種啟發式演算法來說，每一種啟發式演算法有著不同的優點及缺點，本篇論文將整合各項方法發揮其優點，再加上多樣性偵測系統與解的改善操作兩大方式來挑選每一代所合適的演算法，以解決更多相似問題，使整個演算法更健全。最後，我們將幾種知名的資料集，並用本論文所提出的新型演算法來作分群，計算其效能。而這項模擬結果將與k-means、模擬退火(simulated annealing)、禁忌搜尋(tabu search) 及genetic k-means algorithm 做分群結果的比較。實驗結果發現這個新方法在各種資料的分群結果都優於上述四種演算法。

The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros and cons, a hyper-heuristic clustering algorithm based on the diversity detection and improvement detection operators to determine when to switch from one heuristic algorithm to another is presented to improve the clustering result in this paper. Several well-known datasets are employed to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm can provide a better clustering result than the state-of-the-art heuristic algorithms compared in this paper, namely, k-means, simulated annealing, tabu search, and
genetic k-means algorithm.

論文審定書i
誌謝iii
摘要iv
Abstract v
List of Figures ix
List of Tables x
Chapter 1 簡介
1.1 動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 論文貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Chapter 2 文獻探討
2.1 底層啟發式演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 SSBHA類型的演算法. . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 PBHA類型的演算法. . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 混合式演算法(hybrid algorithm) . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 以基因演算法為基礎的混合式演算法. . . . . . . . . . . . . . . . 10
2.2.1.1 SA + GA . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.1.2 KM + GA . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2.2 以粒子最佳化為基礎的混合式演算法. . . . . . . . . . . . . . . . 11
2.2.2.1 SOM→ PSO與PSO→KM . . . . . . . . . . . . . . . . . 11
2.2.2.2 KM→ (NM + PSO)與(ACO + PSO) → KM . . . . . . . 11
2.2.3 以螞蟻演算法為基礎的混合式演算法. . . . . . . . . . . . . . . . 12
2.2.3.1 SA + ACO . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.3.2 KM + ACO與ASCA→AK . . . . . . . . . . . . . . . . . 13
2.3 超啟發式演算法策略. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.1 高層策略的編碼方式. . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.1.1 無編碼情形. . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1.2 有編碼情形. . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.2 簡易選取底層演算法的高層策略. . . . . . . . . . . . . . . . . . . 14
2.3.3 使用啟發式演算法作為高層的策略. . . . . . . . . . . . . . . . . . 15
2.3.4 解的改善偵測策略. . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 混合型演算法與超啟發式演算法的比較. . . . . . . . . . . . . . . . . . . 17
Chapter 3 超啟發式分群演算法
3.1 使用多樣性偵測的超啟發式分群演算法操作方式. . . . . . . . . . . . . . 20
3.2 底層啟發式演算法的資訊分享方式與實作. . . . . . . . . . . . . . . . . . 23
3.3 解的改善偵測操作. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4 解的多樣性偵測系統. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.5 實作例子. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Chapter 4 實驗結果
4.1 執行環境、參數設定、資料集介紹. . . . . . . . . . . . . . . . . . . . . . 30
4.2 模擬結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2.1 單一型啟發式演算法的比較分析. . . . . . . . . . . . . . . . . . . 32
4.2.2 單一型與混合型及單一型與HHCAD的比較分析. . . . . . . . . . 32
4.2.3 與混和型演算法比較分析. . . . . . . . . . . . . . . . . . . . . . . 33
4.3 執行時間. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.4 總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Chapter 5 結論與未來展望
5.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.2 未來展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
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