關鍵字查詢 | 類別:會議論文 | | 關鍵字:Combined Fuzzy-Evolution Method for Multi-Objective Optimization

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序號 學年期 教師動態
1 89/1 電機系 黃聰亮 副教授 會議論文 發佈 Combined Fuzzy-Evolution Method for Multi-Objective Optimization , [89-1] :Combined Fuzzy-Evolution Method for Multi-Objective Optimization會議論文Combined Fuzzy-Evolution Method for Multi-Objective OptimizationChien, Ching-Yang; Hsiao, Ying-Tung; Chen, Chia-Hong; Huang, Tsong-Liang淡江大學電機工程學系模糊集合理論;多目標規劃;演變規劃;饋線重組;Fuzzy Set Theory;Multi-Objective Programming;Evolution Programming;Feeder Reconfiguration中華民國第八屆模糊理論及其應用會議論文集=Proceedings of the 8th National Conference on Fuzzy Theory and Its Application,頁144-149淡江大學 機械工程系; 教育部; 國家科學委員會 Dept. of Mechancial Engineering, Tamkang University, R.O.C.; Ministry of Education; National Science CouncilThis paper presents a combined fuzzy-evolution method formulti-objective optimization. Firstly, the objective functions of theoptimization problem are modeled with fuzzy sets to represent theirimprecise nature. That also enables us to reduce the inaccuracies indecision-makers' judgments. The concept of non-inferiority is employedto characterize a solution of the multi-objective problem . Then, afuzzy satisfied method based on evo
2 89/1 電機系 蕭瑛東 教授 會議論文 發佈 Combined Fuzzy-Evolution Method for Multi-Objective Optimization , [89-1] :Combined Fuzzy-Evolution Method for Multi-Objective Optimization會議論文Combined Fuzzy-Evolution Method for Multi-Objective OptimizationChien, Ching-Yang; Hsiao, Ying-Tung; Chen, Chia-Hong; Huang, Tsong-Liang淡江大學電機工程學系模糊集合理論;多目標規劃;演變規劃;饋線重組;Fuzzy Set Theory;Multi-Objective Programming;Evolution Programming;Feeder Reconfiguration中華民國第八屆模糊理論及其應用會議論文集=Proceedings of the 8th National Conference on Fuzzy Theory and Its Application,頁144-149淡江大學 機械工程系; 教育部; 國家科學委員會 Dept. of Mechancial Engineering, Tamkang University, R.O.C.; Ministry of Education; National Science CouncilThis paper presents a combined fuzzy-evolution method formulti-objective optimization. Firstly, the objective functions of theoptimization problem are modeled with fuzzy sets to represent theirimprecise nature. That also enables us to reduce the inaccuracies indecision-makers' judgments. The concept of non-inferiority is employedto characterize a solution of the multi-objective problem . Then, afuzzy satisfied method based on evo
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