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博碩士論文 etd-0808110-142403 詳細資訊
Title page for etd-0808110-142403
論文名稱
Title
基於峰態評估及維度分割之混合型粒子群演算法
A New Cooperative Particle Swarm Optimizer with Landscape Estimation and Dimension Partition
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
63
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2010-07-26
繳交日期
Date of Submission
2010-08-08
關鍵字
Keywords
維度分割、峰態評估、粒子群演算法
Landscape Estimation, PSO, Dimension Partition
統計
Statistics
本論文已被瀏覽 5896 次,被下載 3143
The thesis/dissertation has been browsed 5896 times, has been downloaded 3143 times.
中文摘要
本研究提出一混合型粒子群演算法,研究中首先建立一評估問題峰態之機制,此機制可辨識問題環境具單峰或多峰趨勢,演算法再針對不同峰態以不同搜尋策略因應,改善過去粒子群演算法無法兼顧單多峰問題環境之障礙。接著研究提出一套雙粒子群並行演化策略,其中一群為完整維度搜尋,另一群為單維度搜尋,此方法可分割解空間,大幅降低高維度問題難度,提升搜尋速度。而在峰態評估與維度分割兩大主要搜尋策略外,研究也建立一組完整的最大速度限制調控機制,此機制藉由觀察粒子目前移動狀態,適時地調整最大速度限制,使粒子搜尋在探索與開發中取得平衡。最後,本研究以此基於峰態評估與維度分割之混合型粒子群演算法,與當代數個優秀粒子群演算法透過37個測試函式實驗比較,結果驗證本方法在不同問題中,都能有相當優異的搜尋速度、準確度與穩定性。
Abstract
This thesis proposes a new hybrid particle swarm optimizer, which employs landscape estimation and the cooperative behavior of different particles to significantly improve the performance of the original algorithm. The landscape estimation is to explore the landscape of the function in order to predict whether the function is unimodal or multimodal. Then we can decide how to optimize the function accordingly. The cooperative behavior is achieved by using two swarms, in which one swarm explores only a single dimension at a time, and the other explores all dimensions simultaneously. Furthermore, we also propose a movement tracking-based strategy to adjust the maximal velocity of the particles. This strategy can control the exploration and exploitation abilities of the swarm efficiency. Finally, we testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other recent variants of the PSO. The results show that our approach outperforms other methods in most of the benchmark problems.
目次 Table of Contents
1緒論 1
1.1研究背景 1
1.2研究動機 1
1.3研究目的與問題 2
2文獻探討 3
2.1演化式演算法 3
2.2原始粒子群演算法 3
2.2.1 Global Best PSO ( gbest PSO ) 4
2.2.2 Local Best PSO ( lbest PSO ) 5
2.2.3 gbest versus lbest PSO 6
2.3 粒子群演算法初步修正 6
2.4數個重要的粒子群演算法版本 7
2.4.1 CPSO 7
2.4.2 FIPS 9
2.4.3 FDR-PSO 9
2.4.4 CLPSO 9
2.4.5 NichePSO 10
3 研究方法 11
3.1峰態評估與相關策略 11
3.1.1單、多峰差異與PSO 11
3.1.2 單多峰評估方式 12
3.1.3單、多峰採用策略 14
3.2 單維度與完整維度之雙PSO演化 15
3.2.1維度分割的必要性 15
3.2.2 CPSO演算法與缺點 15
3.2.3單維度與完整維度之雙PSO演化 15
3.2.4與CPSO演算法之比較與結果 16
3.3最大速度VMAX控制 19
3.3.1 VMAX與PSO 19
3.3.2初始值VMAXi 19
3.3.3遞減方式 19
3.3.4回升方式 20
3.3.5最低速度限制VMIN 21
3.3.6參數組合選擇 21
3.3.7小結 25
3.4混合式PSO之提出 26
4實驗與結果 27
4.1參數設定與實驗環境 27
4.1.1原始PSO參數設定 27
4.1.2本方法新增參數設定 27
4.1.3實驗平台 28
4.2測試函式整理 28
4.3演算法比較一:PSO (標準版) 31
4.3.1演算法簡介 31
4.3.2實驗內容 31
4.3.3實驗結果 31
4.4演算法比較二: APSO 33
4.4.1演算法簡介 33
4.4.2實驗內容 34
4.4.3實驗結果 34
4.5演算法比較三: EPUS 36
4.5.1演算法簡介 36
4.5.2實驗內容 37
4.5.3實驗結果 37
4.6演算法比較四: HAEPSO 38
4.6.1演算法簡介 38
4.6.2實驗內容 39
4.6.3實驗結果 39
4.7演算法比較五: PPSA 44
4.7.1演算法簡介 44
4.7.2實驗內容 44
4.7.3實驗結果 45
4.8小結 46
5結論與未來研究 47
5.1結論 47
5.2未來研究 47
參考文獻 48
附錄一 52
參考文獻 References
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