粒子群集法於圓形物件排列的應用 洪國強、紀華偉
E-mail: [email protected]
摘 要
粒子群最佳化演算法（Particle Swarm Optimization, PSO）是由 Eberhart 和Kennedy 兩位博士所提出的演算法。 PSO 源起 對於鳥群捕 食行為的觀察過程，由簡單的個體組合而成的群體以及個體之間的互動 行為，透過科學模擬系統從局部信息 來產生不可預測的群體行為，藉以 有效達到覓食的目標，同時來簡化說明社會生命現象，避免採取隱喻機 制的演算法來 解決問題。 本論文利用PSO 演算法，以族群為基礎於設計空間中，進行搜尋運 算的特性排列物件，探討PSO 更新機制對 於排列的影響。在結果中得知 ，速度慣性權重（inertia weighted）對於粒子移動的穩定性有關，加 速度常數（ acceleration constant）的大小影響收斂速度的快慢。對 於排列的結果，速度慣性權重與加速度常數影響較不明顯，影響較明顯 的則是 初始產生的位置範圍。
關鍵詞 : 粒子群集法 ; 圓形物件排列 ; 演算法 ; 穩定性 ; 群集法 ; 權重 ; 範圍 目錄
封面內頁 簽名頁 授權書 ...iii 中文摘要 ...v 英文摘要 ...vi 誌謝 ...vii 目錄 ...viii 圖目錄 ...x 表目錄
...xii 第一章 緒論 ...1 1.1 前言 ...1 1.2 研究目的與內容 ...2 1.3 研究方法與架構 ...2 第二章 文獻探討 ...4 第三章粒子群演算法 ...11 3.1 粒子群最佳化演算法簡介 ...11 3.2 粒子群最佳化演算法運算方式 ...12 3.3 PSO 運 算流程與演算法流程圖 ...15 第四章 問題描述與範例 ...17 4.1 問題之定義 ...17 4.2 物 件排列方程式 ...17 4.3 結果與討論 ...20 4.3.1 粒子數 ...22 4.3.2 菁英再啟動 ...28 4.3.3 速度慣性權重 ...30 4.3.4 最佳位置權重係數 ...34 4.3.5 初始排列邊界 ...34 第五章 結論與未來展望 ...42 5.1 結論 ...42 5.1 未來展望
...43 參考文獻 ...44 附錄 ...48 參考文獻
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