查詢結果分析
來源資料
第1筆 /總和 1 筆
/ 1 筆
頁籤選單縮合
題名 | 以螞蟻群聚最佳化整合噪音擾動法求解TSP問題=Sloving TSP Problems by Ant Colony Optimization and Noising Method |
---|---|
作者姓名(中文) | 蘇純繒; 翁瑞聰; | 書刊名 | 商管科技季刊 |
卷期 | 4:4 2003.12[民92.12] |
頁次 | 頁359-375 |
分類號 | 314.5、314.5 |
關鍵詞 | 銷售員旅行問題; 螞蟻群聚最佳化; 模糊理論; 噪音擾動法; Traveling salesman problem; TSP; Ant colony optimization; ACO; Fuzzy theory; Noising method; NM; |
語文 | 中文(Chinese) |
中文摘要 | 螞蟻群聚最佳化(Ant Colony Optimization;ACO)是由Dorigo在1991年所發表的一個新的啟發式演算法,並成功的運用於銷售員旅行問題(Traveling Salesman Problem;TSP)問題上。由於ACO演算法的參數較難控制且會影響求解品質。因此,本研究主要是改善ACO演算法,針對TSP問題進行求解。本研究主要發展兩個演算法:模糊螞蟻群聚最佳解(Fuzzy Ant Colony Optimization;FACO)及噪音螞蟻群聚最佳解(Noising Ant Colony Optimization;NACO)。FACO與NACO模型在經過TSPLIB題庫之國際例題測試後,確實比傳統ACO有較佳的求解品質,且比基因演算法更易收斂且求解品質也較佳。 |
英文摘要 | Ant Colony Optimization (ACO) issued by Dorigo in 1991 is a heuristic algorithm and applied to traveling salesman problem (TSP) successfully. Owing to the parameters of ACO algorithm is hard to be controlled and ACO algorithm would influence the quality of searching for answers; so, the purpose of this study is to improve ACO algorithm and search for answer is accordance with TSP. This main study develops two algorithms: Fuzzy Ant Colony Optimization (FACO) & Noising Ant Colony Optimization (NACO). After testing FACO & NACO model through TSPLIB, it proves they are better than traditional ACO in quality and much easier to converge than Genetic Algorithm (GA) and have better quality. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。