期刊論文
| 學年 | 104 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2015-09-17 |
| 作品名稱 | Minimization of the Total Traveling Distance and Maximum Distance by Using a Transformed-Based Encoding EDA to Solve the Multiple Traveling Salesmen Problem |
| 作品名稱（其他語言） | |
| 著者 | Chen, S. H. |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Mathematical Problems in Engineering 2015, 640231 (13 pages) |
| 摘要 | Estimation of distribution algorithms (EDAs) have been used to solve numerous hard problems. However, their use with in-group optimization problems has not been discussed extensively in the literature. A well-known in-group optimization problem is the multiple traveling salesmen problem (mTSP), which involves simultaneous assignment and sequencing procedures and are shown in different forms. This paper presents a new algorithm, named EDAMLA, which is based on self-guided genetic algorithm with a minimum loading assignment (MLA) rule.This strategy uses the transformed-based encoding approach instead of direct encoding. The solution space of the proposed method is only 𝑛!. We compare the proposed algorithm against the optimal direct encoding technique, the two-part encoding genetic algorithm, and, in experiments on 34 TSP instances drawn from the TSPLIB, find that its solution space is 𝑛! ( 𝑛−1 𝑚−1 ). The scale of the experiments exceeded that presented in prior studies. The results show that the proposed algorithm was superior to the two-part encoding genetic algorithm in terms of minimizing the total traveling distance. Notably, the proposed algorithm did not cause a longer traveling distance when the number of salesmen was increased from 3 to 10. The results suggest that EDA researchers should employ the MLA rule instead of direct encoding in their proposed algorithms. |
| 關鍵字 | |
| 語言 | en |
| ISSN | 1563-5147 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | EGY |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121393 ) |