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姓名	張伯墉(Bo-Yong Jhang)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	適應性自我學習粒子群演算法 (Adaptive Self-Learning Particle Swarm Optimization)
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摘要(中)	於本篇論文中，我們提出一種改良式的粒子群演算法，名稱為適應性自我學習粒子群演算法(Adaptive Self-Learning Particle Swarm Optimization, ASLPSO)，並將其應用於資料分群之問題。本文利用自我學習機制，讓粒子能夠向表現比他更好的其他粒子學習，以獲取有用的資訊，並再透過動態的模式轉換策略改進粒子的搜尋能力，使粒子能在演算法疊代過程的不同階段，轉換其搜尋模式，以提高找到全域最佳解的可能性。我們最後使用16種測試函數進行模擬，與其他已提出的不同改良式粒子群演算法做比較，實驗的結果表示，本文所提出的改良方法可以在大部分的測試函數中有著較佳的表現。最後並將本文的改良式演算法運用在資料分群的問題上，我們可以在某些性能指標上得到更好的結果，但也有較差的部分，這顯示本文的方法仍有進一步改善的可能。
摘要(英)	This thesis proposes a new particle swarm optimization (PSO) called Adaptive Self-Learning Particle Swarm Optimization (ASLPSO), and applies it to the classification problem. A self-learning method is introduced in the ASLPSO that every particle randomly selects its learning object among the better particles to acquire useful information. We also designs a dynamic transition strategy to improve the searching approach of particles during the iterations. In the experiments, the performance of the proposed ASLPSO is compared to several improved PSO’s in the literature by testing sixteen benchmark functions. The experimental results show that the proposed algorithm performs better on most of the functions. At last, the ASLPSO is applied to a classification problem. In our experiments, many classification results are better, but not all. To be more precisely, the ASLPSO is supposed to be refined in some ways.
關鍵字(中)	★ 粒子群演算法 ★ 資料分群 ★ K-means演算法 ★ 群集分析	關鍵字(英)	★ particle swarm optimization ★ data clustering ★ K-means clustering ★ cluster analysis
論文目次	摘要 I Abstract II 致謝 III 目錄 IV 圖目錄 VI 表目錄 IX 第一章 緒論 1 1.1研究動機 1 1.2論文架構 3 第二章 粒子群演算法 4 2.1傳統粒子群演算法 4 2.2傳統粒子群演算法的基本形式 4 2.3慣性權重 5 第三章 適應性自我學習粒子群演算法 9 3.1資訊的傳遞 9 3.1.1粒子的經驗交流 9 3.1.2自我學習機制 10 3.2搜尋模式的轉換策略 14 3.3適應性自我學習粒子群演算法 15 3.4 學習機制的比較 17 3.4.1 10維的比較 22 3.4.2 30維的比較 24 第四章 實驗結果 26 4.1 目標函數 26 4.2 參數設定與測試方法 30 4.2.1 測試函數在10維下的結果 31 4.2.2 測試函數在30維下的結果 42 第五章 適應性自我學習粒子群演算法於資料分群之應用 54 5.1 K-means演算法 54 5.2將粒子群演算法應用於資料分群問題 56 5.3以適應性自我學習粒子群演算法進行資料分群 57 5.4模擬結果 58 第六章 總結與未來展望 63 6.1總結 63 6.2未來展望 63 參考文獻 64
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指導教授	莊堯棠(Yau-Tarng Juang)	審核日期	2016-8-8
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