Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/1925
DC FieldValueLanguage
dc.contributor.advisor陳定宇zh_TW
dc.contributor.advisorChen, T.Y.en_US
dc.contributor.author陳信昌zh_TW
dc.contributor.authorChen, H.C.en_US
dc.date2006zh_TW
dc.date.accessioned2014-06-05T11:42:03Z-
dc.date.available2014-06-05T11:42:03Z-
dc.identifier.urihttp://hdl.handle.net/11455/1925-
dc.description.abstract摘要 比起其他演化式最佳化方法,RNES在適應值的計算上相對的簡單,個體的適應值計算方式是基於個體在各目標下的排名和擁擠程度而定。但RNES在一些參數的設定使用或機制的選用上仍有改善空間,因此本文將嘗試修改、新增演化過程中的參數與機制,希望達到改善演化效率,提供穩定的求解能力。這些修改包括了對重組方式的新增、重組率與突變率的引用、外部菁英族群數量的控制機制改善等,同時也進行題目的測試與效能評估。經過不同類型、目標數的題目測試後,新增或修改後的RNES都有不錯的效率改善與求解能力。 實際上的最佳化問題設計變數大多是受到數量與範圍拘限的離散變數,因此本文還針對RNES設計了三種處理離散變數的方法,另外也引用在其他演化式方法中使用的離散變數處理方法卜瓦松分佈亂數方法共四種方法,使得RNES除了可以處理實數變數之外,還可以同時處理離散變數或是混合離散、整數、連續變數等問題。本文以數個不同複雜程度的題目進行測試,測試的結果顯示,RNES配合本文所提出的三種處理離散變數方法在處理具離散變數或混合變數的最佳化問題上都有不錯的求解能力與效率。zh_TW
dc.description.abstractAbstract Compared with other evolutionary algorithms the fitness computation in multiobjective solver RNES is relatively simple. The fitness is computed based on the ranks and the crowding status of the individual. But the parameter settings and the evolutionary operators still have rooms to improve. This thesis tries to eliminate the drawbacks of RNES to increase the efficiency and capability of finding better solutions. These efforts include adding new recombination operators, introducing mutation probability and recombination probability and simplifying clustering operation. Some problems are used to test modified RNES and the results are satisfactory. Many real-life optimization problems contain discrete variables and constraints. In addition to previous improvements this thesis also introduces three methods to treat discrete variable problems. Besides those three methods developed in this thesis one method from other paper using random number of Poisson distribution to treat discrete variables is also tested. The RNES with these discrete variables treating methods can solve not only continuous variable problems but also mixed-variable problems. Several test problem with different characteristics are used to test the methods proposed in this thesis. In general the outcomes show the methods proposed indeed can solve those problems efficiently.en_US
dc.description.tableofcontents目 錄 致謝 中文摘要…………………………..………………………………….i 英文摘要……………………………………………………………...ii 目錄…………………………………………………………………...iii 圖目錄……………………………………………………………....vi 表目錄……………………………………………………………... xv 符號說明………………………………………………………....xvii 第一章 緒論 1.1 前言………………………….………………………………1 1.2 文獻回顧……………………………………………………2 1.3 研究動機與目的……………………………………………5 1.4 研究方法與內容……………………………………………5 第二章 多目標演算法與RNES方法 2.1 多目標最佳化問題概論……………...……………………7 2.2 演化式計算發展概論………….…………………………...13 2.3 演化策略法簡介…………...……………………………..22 2.4 RNES………………………..…………………………….27 第三章 RNES機制和策略之改善與題目測試 3.1 RNES重組機制的新增…………………………….…….33 3.2 無限制條件單目標最佳化測試問題…………...………..35 3.3 無限制條件多目標最佳化測試問題…………………….49 3.4 重組率與突變率的使用測試……………………………100 3.5 外部菁英族群數量控制方法的改善…..…………………111 第四章 RNES處理離散變數的方法與驗證 4.1 離散變數、離散隨機變數與離散最佳化問題……………125 4.2 RNES處理離散的方式…………………………………...127 4.3 離散變數與混雜變數最佳化的測試題目………………..132 4.4 多目標混雜變數最佳化測試題目……….………………167 4.5 結論………………...……………………………………...178 第五章 結論與未來展望 5.1結論……………………………………………………….180 5.2 未來發展與建議……………………………………….….183 參考文獻…………………………………… ……….………………….184 附錄A 常態分佈(Normal distribution)…………………………..…….190 附錄B 卜瓦松分佈(Poisson distribution)…………………………......194 附錄C Box plot說明…………………………………………………..196zh_TW
dc.language.isoen_USzh_TW
dc.publisher機械工程學系zh_TW
dc.subject演化式最佳化zh_TW
dc.subjectRNESzh_TW
dc.subject重組率zh_TW
dc.subject突變率zh_TW
dc.subject離散變數zh_TW
dc.subject混合變數zh_TW
dc.title可處理離散和混合變數之演化策略法zh_TW
dc.titleDiscrete and Mixed-Variable Evolution Strategyen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.grantfulltextnone-
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