臺灣博碩士論文加值系統

國土防衛作戰時，我軍將聯合陸、海、空軍，運用多種武器系統編成多層火網逐步削弱敵軍進攻能量。有效的火力分配是部署多層攔截火網的成功關鍵，涉及軍事作業研究中典型的武器-目標分配 (WTA) 問題。
接戰中，武器更改射擊目標將引發有形的成本及無形的風險，武器-目標分配時應盡量避免，然而，減少武器的目標轉換將限制其作戰效果的發揮。本研究以各階段間武器-目標分配的差異程度視為武器目標轉換的成本，建構多目標武器-目標分配最佳化模型，同時考慮最小化來襲目標的總期望威脅值（武器作戰效果）與武器-目標在各階段配對的差異程度（目標轉換成本），並提出分解式多目標簡群演算法（命名為dMOSSO）對問題求解。
為了評估dMOSSO的演算效能及效率，本研究基於建構的模型，設計了27組武器-目標分配問題，由dMOSSO與其他4種常用的多目標進化式演算法進行求解並比較結果。依據計算資源的充裕程度，演算法的比較區分兩部份：充裕與有限的計算資源下的比較，前者以演算法足夠收斂的適應函數的評估次數作為停止條件，後者則以有限的演算時間作為停止條件。無論在何種計算資源下，dMOSSO在求解效能、搜尋非支配解的品質及效率均優於其他演算法。

When homeland defense begins, our armed forces utilize all available weapons which constitute a multilayer defense system counteracting and eliminating the incoming targets. Effective weapon assignment is the key to success for the multilayer defense system and involves the weapon-target assignment (WTA) problem which is a classical constrained combinatorial optimization problem arising the field of military operations research.
In a continuous fire engagement, a fire transferring is caused by direction and position changes of targets. It results in firing data corrections (tangible costs) and even the translocation of weapons (intangible risks). Thus, the fire transferring should be avoided as possible. However, the decrease of the fire transferring will impact shooting ability and operational effectiveness of weapons. Therefore, the WTA problem considered in this study is formulated into a multi-objective optimization problem which employs the minimum of the expected threat value of incoming targets and minimum of the costs of the fire transferring as two competing objective functions. In addition, a novel decomposition-based Multi-Objective Simplified Swarm Optimization, known as dMOSSO, is proposed for solving this problem.
To empirically evaluate the performance of the proposed method, experiments are conducted using twenty-seven randomly generated multi-objective WTA problems ranging from small to large scale, and the corresponding results are compared with four state-of-the-art methods. Numerical results show that dMOSSO outperforms its competitors on solving multi-objective WTA problems discussed in this study.

目次
謝辭
摘要
Abstract
目次
表次
圖次
第一章 緒論
1.1 研究背景
1.2 研究動機與目的
1.3 研究範圍與限制
1.4 研究流程
第二章 文獻探討
2.1 武器-目標分配問題 (weapon-target assignment, WTA)
2.1.1 WTA定義與分類
2.1.2 SWTA文獻回顧
2.1.3 DWTA文獻回顧
2.1.4 多目標DWTA (MODWTA)
2.1.5 小結
2.2 多目標最佳化
2.2.1 多目標最佳化問題
2.2.2 多目標最佳化方法
2.2.3 多目標進化式演算法
2.2.4 基於分解的多目標進化式演算法
2.2.5 小結
2.3 簡群演算法 (Simplified Swarm Optimization, SSO)
2.3.1 更新原理
2.3.2 簡群演算法的發展及其應用
2.3.3 離散簡群演算法的改進策略
2.3.4 多目標簡群演算法(Multi-Objective Simplified Swarm Optimization, MOSSO)
2.3.5 小結
第三章 模型建構
3.1 假設及符號說明
3.2 模型建構原則
3.3 模型說明
第四章 研究方法建構
4.1 多目標最佳化架構
4.1.1 分解機制
4.1.2 更新機制
4.1.3 領導解機制
4.1.4 非支配解維持機制
4.2 演算法步驟
4.2.1 dMOSSO步驟
4.2.2 演算法流程圖
4.3 dMOSSO求解演示
4.3.1 問題範例
4.3.2 解編碼
4.3.3 計算適應函數值
4.3.4 更新群體
第五章 研究方法評估
5.1 實驗設計
5.1.1 績效指標
5.1.2 資料集產生說明
5.2 領導解機制選擇
5.2.1 機制比較
5.2.2 K近鄰敏感性分析
5.3 dMOSSO評估
5.3.1 充裕的計算資源下比較
5.3.2 有限的計算資源下比較
5.4 統計分析
5.5 多準則決策
第六章 結論與建議
6.1 研究貢獻
6.2 未來研究方向
參考文獻
英文文獻

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