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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chen Qian Yasuda, K. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. & Electron. Eng., Tokyo Metropolitan Univ., Hachioji (Chen Qian; Yasuda, K.) |
| Abstract | In this paper, we propose a new particle swarm optimization (PSO), which is based on successive optimization in its parameter space, in order to overcome the difficulty for applying PSO to complex and high dimensional nonlinear optimization problems. The proposed PSO consists of two types of optimization procedures; optimization in its decision variable space and optimization in its parameter space. Some numerical simulations using 6 types of typical benchmark problems verify the performance of the proposed PSO. |
| Starting Page | 932 |
| Ending Page | 937 |
| File Size | 266654 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424423835 |
| ISSN | 1062922X |
| DOI | 10.1109/ICSMC.2008.4811400 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-12 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle swarm optimization Optimization methods Algorithm design and analysis Numerical simulation Genetic algorithms Simulated annealing Genetic engineering Design engineering Stochastic processes Animals Parameter Setting Global Optimization Particle Swarm Optimization Decent Method Successive Optimization |
| Content Type | Text |
| Resource Type | Article |
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