期刊論文
| 學年 | 94 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2005-10-01 |
| 作品名稱 | Two Phase Sub-Population Genetic Algorithm for Parallel Machine Scheduling problem |
| 作品名稱（其他語言） | |
| 著者 | Pei-Chann Chang; Shih-Hsin Chen; Kun-Lin Lin |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Expert Systems with Applications 29(3), p.705-712 |
| 摘要 | This paper introduces a two‐phase sub population genetic algorithm to solve the parallel machine-scheduling problem. In the first phase, the population will be decomposed into many sub-populations and each sub-population is designed for a scalar multi-objective. Sub-population is a new approach for solving multi-objective problems by fixing each sub-population for a pre-determined criterion. In the second phase, non-dominant solutions will be combined after the first phase and all sub-population will be unified as one big population. Not only the algorithm merges sub-populations but the external memory of Pareto solution is also merged and updated. Then, one unified population with each chromosome search for a specific weighted objective during the next evolution process. The two phase sub-population genetic algorithm is applied to solve the parallel machine-scheduling problems in testing of the efficiency and efficacy. Experimental results are reported and the superiority of this approach is discussed. |
| 關鍵字 | Scheduling problem;Genetic algorithm;Multi-objective optimization;Evolution strategy |
| 語言 | en |
| ISSN | 0957-4174; 1873-6793 |
| 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 | GBR |
| 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/987654321/121463 ) |