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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiangshao Gu Kunmei Wen |
| Copyright Year | 2014 |
| Description | Author affiliation: Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China (Jiangshao Gu; Kunmei Wen) |
| Abstract | Since the original Glowworm Swarm Optimization (GSO) contains the defects of being caught in local optima potentially and slow convergence rate, we analyze the superiority of quantum system and introduce this technique into the behavior of glowworms according to local conditions, to propose Quantum-behaved Glowworm Swarm Optimization (QGSO). By a series of improvements, the diversity of swarms is enhanced and the oscillation caused by constant step length is eliminated. Large experiments were conducted and it is illustrated that QGSO performs consistently to keep a better balance between exploration and exploitation, and evolves faster compared with the existing competitors. |
| Starting Page | 430 |
| Ending Page | 436 |
| File Size | 496882 |
| Page Count | 7 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781479951512 |
| DOI | 10.1109/ICNC.2014.6975874 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | glowworm swarm optimization Benchmark testing quantum-behaved Wave functions Mathematical model Particle swarm optimization meta-heuristics Optimization Equations optimization problems Convergence |
| Content Type | Text |
| Resource Type | Article |
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