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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaoling Wu Min Zhong |
| Copyright Year | 2009 |
| Description | Author affiliation: School of Computer, Wuhan University, Wuhan, China (Xiaoling Wu; Min Zhong) |
| Abstract | Particle Swarm Optimization (PSO) has shown its fast search speed and good search ability in many optimization problems. However, PSO easily suffers from local minima when dealing with complex problems. In order to enhance the standard PSO, this paper presents an improved PSO algorithm, namely PMPSO, which employs a power mutation (PM). The main idea of PMPSO is to conduct a PM on the global best particle in current swarm. It is to hope that the mutation could help particles jump out local optima. To verify the performance of the proposed approach, PMPSO is compared with some existing algorithms, PSO with Cauchy mutation (HPSO), PSO with near neighbor interactions algorithm (FDR-PSO), classical evolutionary programming (CEP), and fast evolutionary programming (FEP) on ten well-known benchmark functions. Experimental results show that PMPSO achieves better results on majority of test functions. |
| Starting Page | 464 |
| Ending Page | 467 |
| File Size | 479886 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424442478 |
| DOI | 10.1109/CCCM.2009.5267559 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-08 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Genetic mutations power mutation Communication system control Evolutionary computation function optimization particle swarm optimization Particle swarm optimization Power engineering computing Engineering management Genetic programming Benchmark testing Functional programming Energy management |
| Content Type | Text |
| Resource Type | Article |
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