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研究生:王亮達
研究生(外文):LIANG-DA WANG
論文名稱:使用PSO-GA演算法運用於TSP問題上做移動式機器人路徑最佳化
論文名稱(外文):An application of the PSO-GA algorithm in the TSP problem and mobile robot path optimization
指導教授:王啟州
指導教授(外文):Chi-Jo Wang
學位類別:碩士
校院名稱:南台科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:100
畢業學年度:99
語文別:中文
論文頁數:60
中文關鍵詞:TSP問題基因演算法機器人路徑規劃粒子群演算法高效率最佳化
外文關鍵詞:Traveling salesman problemGenetic algorithmParticle swarm optimization AlgorithmRobot route planning, Optimization
相關次數:
  • 被引用被引用:4
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  • 下載下載:270
  • 收藏至我的研究室書目清單書目收藏:1
旅行推銷員問題(Traveling salesman problem, TSP)最早是美國軍方建立全國通訊路線時所延伸出來的問題。TSP目前已經被證實為NP-Complete問題,由於定義簡單、複雜度高,許多新型演算法都以其為測試標準,目前這類問題已經有高效率演算法能獲得很好的解答。
本論文提出一套以粒子群演算法為主結合基因演算法之理論所發展出來的移動式機器人之最佳路徑系統,利用基因演算法來調整粒子群演算法之權重值使移動式機器人在環境上選擇最佳的路徑,並且使移動式機器人的行動路線為最流暢動線,進而使移動式機器人在行走上更加平穩且更有效率,最後再將此研究方法運用於TSP問題,來應證本方法之效能。
The Traveling salesman problem (TSP) was first seriously studied when the US military attempted to set up a nation-wide communication network. It is well known to be NP-Complete. In view of its simple definition and challenging computation, TSP is often regarded as a benchmark in the evaluation of algorithms. Algorithms of high efficiency have been developed for TSP.
This thesis aims to develop a methodology for mobile robot path optimization based on a combination of Particle Swarm Optimization (PSO) Algorithm and Genetic Algorithm (GA). GA was used to tune the weightings in PSO to achieve a smooth and efficient path. The efficacy of the proposed algorithm was also demonstrated in solving TSP.
摘要 iv
誌謝 vi
目次 vii
表目錄 ix
圖目錄 x
第一章 序論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 文獻探討 5
1.4 論文架構 8
第二章 TSP問題之介紹及運用 9
2.1 TSP問題簡介 9
2.2 各演算法運用於TSP問題之介紹 16
第三章 PSO-GA法運用於TSP問題之應用 19
3.1 粒子群演算法 19
3.2 基因演算法 26
3.3 PSO-GA法運用於TSP問題 31
第四章 實驗結果 36
4.1 PSO-GA法在TSP上之表現 36
4.2 將PSO-GA法運用於模擬機器人上做最進化路徑規劃 42
第五章 結論與未來展望 49
第六章 參考文獻 50
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