由於大部分的啟發式演算法在設計時，並未考慮應用於分散式計算的環境。在求解大量且複雜的最佳化問題時，通常需要耗費大量的計算成本，因此目前在即時應用的議題上成果較少。近期雲端計算的平台及環境日益成熟，本研究嘗試將珊瑚礁演算法以 MapReduce 架構，將運算工作分配給各節點進行平行化運算，並將其實作於 Spark 系統上，加速啟發式演算法。此外，本研究並將改良全域搜尋與區域搜尋步驟，減少傳輸資料的消耗，使之能更有效率的使用各個節點的計算資源。實驗結果顯示，本論文所提之平行化珊瑚礁演算法，可在大型的工作排程問題，於較短的時間內找到較好的排程結果。

Since most metaheuristic algorithms are not designed for distributed computing environments, they will normally take a lot of computation cost for large-scale and complex optimization problems. That is why not many successful results are out there for real-time applications. With the advance of cloud computing, this study is thus aimed at building the so-called coral reef optimization (CRO) on the MapReduce framework so as to distribute the computation tasks to a set of cluster nodes and to realize it on Spark to speed up its response time. Moreover, the global and local search operators of the CRO are enhanced to reduce the amount of data transmitted so that the computation resources of each cluster node are more efficiently used. The simulation results show that the proposed algorithm is able to find a better result than the original CRO for large-scale job shop scheduling problems.

論文審定書 i
Acknowledgments iv
摘要 v
Abstract vi
List of Figures x
List of Tables xii
Chapter 1 簡介 1
1.1 動機 2
1.2 論文貢獻 2
1.3 論文架構 3
Chapter 2 相關文獻探討 4
2.1 叢集式電腦 4
2.1.1 MapReduce 5
2.1.2 Apache Hadoop 6
2.1.3 Apache Spark 7
2.1.3.1 Spark on YARN 9
2.1.3.2 彈性分散式資料集 10
2.2 工作排程問題 12
2.3 基因演算法 13
2.4 禁忌搜尋演算法 14
2.5 珊瑚礁演算法 14
2.5.0.3 演算法流程 15
2.5.0.4 演算法模擬 17
2.6 結論 17
Chapter 3 平行化珊瑚礁演算法 19
3.1 演算法設計概念 20
3.2 演算法流程 21
3.2.1 演算法機制
3.2.1.1 初始化
3.2.1.2 交配繁殖
3.2.1.3 無性生殖
3.2.1.4 適應
3.2.2 key-value 配對 25
3.2.3 Evolution 模組 25
3.2.4 競爭操作 26
3.2.5 捕食操作 27
3.3 演算法範例 28
3.3.1 初始化範例 28
3.3.2 平行化珊瑚礁演算法範例 28
Chapter 4 實驗結果 31
4.1 實驗環境 31
4.2 模擬結果 32
4.2.1平行化珊瑚礁演算法與單機版演算法之分析 33
4.2.1.1 資料集規模分析 35
4.2.2 平行化珊瑚礁演算法與平行化基因演算法之比較分析 39
4.2.3 使用不同數量之運算節點的運行效率 41
4.2.4 平行化負載率 41
4.3 總結 43
Chapter 5 結論與未來展望 45
5.1 結論 45
5.2 未來展望 46
Bibliography 47

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