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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gao Wei |
| Copyright Year | 2007 |
| Description | Author affiliation: Wuhan Polytech. Univ., Wuhan (Gao Wei) |
| Abstract | Nowadays, to solve continuous optimization problem and extend the traditional ant colony algorithm, some continuous ant colony algorithms have been proposed. To improve the searching performance, the principles of evolutionary algorithm and immune algorithm have been combined with the typical continuous ant colony algorithm, and one new immunized continuous ant colony algorithm is proposed here. In this new algorithm, the ant individual is transformed by adaptive Cauchi mutation and thickness selection. To verify the new algorithm, the typical functions, such as Schaffer function and "needle-in-a-haystack" function, are all used. The results show that, the convergent speed and computing precision of new algorithm are all very good. |
| Starting Page | 705 |
| Ending Page | 709 |
| File Size | 535475 |
| Page Count | 5 |
| File Format | |
| ISBN | 9787811240559 |
| DOI | 10.1109/CHICC.2006.4346802 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-07-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ant colony optimization Immune algorithm Genetic mutations Continuous optimization Evolutionary algorithm Evolutionary computation Immunized continuous ant colony algorithm Continuous ant colony algorithm |
| Content Type | Text |
| Resource Type | Article |
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