當今無線感測網路 (Wireless Sensor Network; WSN) 已被廣泛地使用於監視與監測等用途，許多應用服務都建立在這架構之上，提供人們一個方便又舒適的環境，如智慧電網。由於無線感測器時常受到電量的限制，無法長時間進行資料傳輸，因此一個有效的演算法將能夠找出距離較短的傳送路徑，使無線感測器的電量消耗減少，進而延長無線感測網路的生命週期。低電量適應性分群階層法 (Low-Energy Adaptive Clustering Hierarchy; LEACH) 為無線感測網路著名的分群演算法之一，雖然該方法讓全部的無線感測器都有機會成為叢集頭 (Cluster Head; CH)，避免電量消耗過於迅速，但是在選擇叢集頭上仍有很大的改善空間。本論文提出一個超啟發式分群演算法 (Hyper-Heuristic Clustering Algorithm; HHCA)，透過調整叢集頭的分佈位置，減少無線感測網路的電量消耗，同時平衡無線感測器所剩餘的電量，讓全部的無線感測器都盡可能地存活下來。實驗結果顯示，本論文所提出的方法可以在無線感測器的存活數量和剩餘電量上，取得比其他分群演算法好的結果，並且適應於各種不同條件的環境。

Wireless sensor network (WSN) has been widely used in surveillance and monitoring nowadays. As such, a lot of applications and services are built on it to provide people a convenient and comfortable environment, such as smart grid. However, the energy of each sensor of a WSN is normally too limited to sustain the transmission of data for a long time. Therefore, an effective algorithm will find a better solution (e.g., a shorter path) to deliver the data so that the energy consumption of each wireless sensor will be reduced, thus prolonging the lifetime of a WSN. One of the well-known algorithms for this problem is the so-called low-energy adaptive clustering hierarchy (LEACH). Although LEACH allows all the wireless sensors to become a cluster head (CH) to avoid the energy of each sensor from being consumed quickly, there is still a plenty of room to improve the quality of the solution. In this thesis, a hyper-heuristic clustering algorithm (HHCA) is presented to reduce the energy consumption of a WSN, by changing the distribution of CHs. The proposed algorithm is also aimed to keep all the sensors of a WSN alive for as long as possible, by balancing the residual energy of all the sensor nodes. Experimental results show that HHCA can find a better result than the other clustering algorithms compared in this thesis for WSN with different kinds of environments, in terms of the number of alive nodes and the remaining energy.

論文審定書 i
誌謝 iv
摘要 v
Abstract vi
List of Figures ix
List of Tables x
Chapter 1 簡介 1
1.1 動機 3
1.2 論文貢獻 4
1.3 論文架構 4
Chapter 2 文獻探討 5
2.1 無線感測網路特色與現況 5
2.2 叢集頭選擇問題 8
2.3 無線感測網路分群演算法 10
2.3.1 低電量適應性分群階層法 10
2.3.1.1 設定階段 12
2.3.1.2 穩定階段 13
2.3.2 基於基因演算法之低電量適應性分群階層法 14
2.3.3 其他延伸相關演算法 16
2.4 超啟發式演算法及其延伸演算法 19
2.4.1 超啟發式演算法 19
2.4.2 其他延伸相關演算法 23
2.5 總結 26
Chapter 3 研究方法 27
3.1 基本理念 27
3.2 超啟發式分群演算法 28
3.2.1 編碼方式 30
3.2.2 解提升偵測運算子 31
3.2.3 多樣性偵測運算子 32
3.2.4 紀錄池運算子 33
3.2.5 其他運算子 35
3.3 範例 37
Chapter 4 實驗結果 40
4.1 執行環境與參數設定 40
4.2 參數影響分析 43
4.2.1 參數 ∅ni 43
4.2.2 參數 ∅max 44
4.2.3 參數 r 45
4.2.4 參數 pp 46
4.2.5 參數 pb 47
4.2.6 參數結果探討 48
4.3 模擬結果 48
4.3.1 收斂 56
4.3.2 多樣性偵測運算子影響分析 57
4.4 總結 60
Chapter 5 結論與未來展望 61
5.1 結論 61
5.2 未來展望 61
Bibliography 63

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