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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jun Ouyang Gui-Rong Yan |
| Copyright Year | 2004 |
| Description | Author affiliation: Mechanical structure Strength & Vibration Laboratory, Xi'an Jiaotong Univ., China (Jun Ouyang; Gui-Rong Yan) |
| Abstract | As a new class of global searching algorithms, ant colony system algorithm could solve TSP (traveling salesman problem). These algorithms includes ACS, MAX-MIN ant system, et al. This work presents a new method named multi-group ant colony system algorithm. This algorithm avoids some defects of ACS and MAX-MIN ant system. These defects make algorithm not iterate when it has arrived at the stagnating state of the iteration or local optimum point. But for multi-group ant colony system, it creates new groups of ants to iterate when meeting those states. In this paper, a simple convergence proof is presented. At the end of this paper, the experimental result is presented to show the effectiveness of this method. |
| Starting Page | 117 |
| Ending Page | 121 |
| File Size | 300514 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780384032 |
| DOI | 10.1109/ICMLC.2004.1380626 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-08-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ant colony optimization Decision making Convergence Traveling salesman problems Chemicals Vibrations Laboratories Computational modeling Machine learning Cybernetics |
| Content Type | Text |
| Resource Type | Article |
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