本研究使用粒子群最佳化平行演算法 ( Particle Swarm Optimization based Parallel Algorithm, PSOPA ) 針對照明控制管理問題求最低能耗解，並且實作在單一機器 ( S-PSOPA ) 與多台機器 ( MR-PSOPA ) 上，值得注意的是本實作S-PSOPA與MR-PSOPA於能耗方面的實驗結果完全一致並在此前提下比較執行時間上的優劣，除此之外模擬更複雜的問題接續比較執行時間與所求能耗。
MR-PSOPA採用雲端運算中屬於開放原始碼的Hadoop架構做為建置平台並且利用HDFS處理PSOPA中必要的資料傳遞行為，最後將PSOPA實作於MapReduce框架上。實驗結果顯示MR-PSOPA在粒子群數量足夠多的情況下其執行時間比S-PSOPA來得少並且當問題變得複雜時其執行時間更明顯少於S-PSOPA。此外，由實驗結果得知MR-PSOPA能處理S-PSOPA無法處理的更為複雜的照明管理控制問題。

In this study, we apply a Particle Swarm Optimization based Parallel Algorithm ( PSOPA ) to deal with the problem of lighting control management for seeking the lowest power consumption. The algorithm is implemented on standalone ( S-PSOPA ) as well as cluster platforms ( MR-PSOPA ). The two versions produce identical results with respect to power consumption. Under the condition of getting identical results, S-PSOPA and MR-PSOPA are compared on efficiency for complex lighting control problems.
MR-PSOPA is implemented in mapreduce and information delivery between subgroups is accomplished by HDFS in Hadoop which is open source in the field of cloud computing. Experimental results show that MR-PSOPA performs better than S-PSOPA in terms of execution time when the number of particles is large enough. Furthermore, MR-PSOPA can robustly solve more complex problems which S-PSOPA may have difficulty with.

致謝 i
摘要 ii
Abstract iii
圖目錄 vi
表目錄 viii
第一章 導論 1
1.1. 研究背景與目的 1
1.1.1. 照明控制 1
1.1.2. 研究目的 2
1.1.3. 問題說明 2
1.2. 論文架構 5
第二章 文獻探討 6
2.1. 粒子群最佳化演算法 6
2.1.1. 粒子群演算法的組成要件 6
2.1.2. 粒子群演算法流程(如圖 2 1所示) 6
2.2. Hadoop 7
2.2.1. MapReduce 8
2.2.2. HDFS 10
第三章 研究方法 11
3.1. PSOPA 11
3.1.1. 粒子群位置及速度的更新 11
第四章 實驗結果與分析 16
4.1. 實驗資料 16
4.2. 實驗一 16
4.2.1. 實驗環境架設 16
4.2.2. 實驗一結果及分析 16
4.3. 實驗二 22
4.3.1. 實驗環境架設 22
4.3.2. 實驗二結果及分析 22
第五章 結論與未來展望 45
5.1. 結論 45
5.2. 未來研究方向 45
參考文獻 46

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