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 本篇論文針對飯店訂房的配置問題，提出一個融合型蝙蝠演算法(Merged Bat Algorithm，MBA)，其目的是要在有限的計算時間內找出一組最佳配置解，使得飯店訂房問題獲得最大利潤。融合型蝙蝠演算法改進了原有蝙蝠演算法(Bat Algorithm，BA)收斂速度慢的缺點，主要控制參數為慣性權重值、響度和脈衝發射率，此三項控制參數會隨著迭代次數增加而進行改變，以加快搜尋最佳解的速度。蝙蝠演算法是一種群體智能演算法，將最佳化問題的可行解當作是搜索空間中的微蝙蝠，搜索最佳解的過程看成是微蝙蝠尋找食物的過程。接著將所提出的融合型蝙蝠演算法，應用在飯店訂房限制收益最佳化問題以測試效能，飯店訂房限制收益最佳化問題是屬於困難的隨機模擬最佳化問題，具有很大的解空間。最後將所提出的演算法與演化式策略(ES)、模擬退火法(SA)、人工蜂群演算法(ABC)以及原始的蝙蝠演算法(BA)四種演算法進行比較，由模擬數據顯示所提出的蝙蝠演算法，不論在解的品質和計算效率上，都能獲得很好的測試結果。
 In this thesis, a Merged bat algorithm, abbreviated as MBA, is proposed to solve the hotel booking limits problem. The goal is to search for a good enough solution with the objective of maximizing the expected revenue using limited computation time. The MBA improves the shortcomings of the slow convergence of the original bat algorithm. The control parameters contain the inertia weight, loudness and pulse emission rate. These three control parameters are varied to speed up the search for the optimal solution when the number of iterations are increased.BA is inspired by the echolocation behavior of microbats, with varying pulse rates of emission and loudness. Then the proposed MBA is applied to a hotel booking limits problem, which is formulated as a hard stochastic simulation optimization problem that consists of a huge solution space comprised by the vector of booking limits. Finally, the proposed MBA is compared with the evolutionary strategies, simulated annealing, artificial bee colony and original bat algorithm. The vector of good enough booking limits obtained by the proposed MBA is promising in the aspects of solution quality and computational efficiency.
 第一章、緒 論 11.1背景 11.2研究動機與目的 31.3研究方法與論文架構 5第二章、進化式演算法群體智能演算法 72.1演化式策略(Evolution Strategy, ES) 72.2 模擬退火法(Simulated Annealing, SA) 92.3 人工蜂群演算法(Artificial Bee Colony algorithm, ABC) 112.4 蝙蝠演算法(Bat algorithm, BA) 132.4.1傳統蝙蝠演算法(BA) 132.4.2增強蝙蝠演算法(EBA) 162.4.3改良型離散蝙蝠演算法(IBA) 182.4.4蝙蝠演算法處理過多分配問題(Bat algorithm for RAP) 19第三章、融合型蝙蝠優化演算法 203.1傳統蝙蝠演算法 203.2融合型蝙蝠演算法 223.3範例步驟說明……………………………………………………………………………27第四章、飯店訂房配置問題與實驗結果比較 454.1　飯店之起源、定義 454.2　飯店訂房之意義 494.3　飯店訂房之形式 504.4　問題定義及數學式 514.5實驗說明 574.6 演算法步驟與參數設定 584.7實驗結果 62第五章、結論 67參考文獻 69圖目錄圖 1. 近十年來臺旅客及國民出國人次變化 3圖 2. 近十年觀光外匯收入、國人國內旅遊收入及觀光總收入 4圖 3. 論文架構 6圖 4. 演化式策略(ES)計算流程 8圖 5. 模擬退火(SA)計算流程 10圖 6. 人工蜂群演算法(ABC)計算流程 12圖7. 蝙蝠演算法流程圖 15圖8. 融合型蝙蝠演算法流程圖 24圖 9. 飯店訂房問題輸入、輸出關係 56圖10. 慣性權重(W)、脈衝發射率(r)和響度(A)圖形 67圖 11. 演算法的利潤值比較 68表目錄表 1. 參數設定 27表 2. 搜尋最佳解 43表 3. 更新訂房限制的三種產品 53表 4. 矩陣A保留現場入宿的資源 56表 5. 模擬退火法(SA)之參數設定 57表 6. 演化式策略(ES)之參數設定 54表 7. 人工蜂群演算法(ABC)之參數設定 60表 8. 實驗環境 61表 9. 慣性權重最小與最大調整( 與 )之比較 62表 10. 響度最小與最大調整( 與 )之比較 63表 11. 脈衝發射率最小與最大調整( 與 )之比較 63表 12. 迭代次數 65表 13. 迭代次數 65表 14. 迭代次數 65表 15. 迭代次數 66
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