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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yuhui Shi Krohling, R.A. |
| Copyright Year | 2002 |
| Description | Author affiliation: EDS Embedded Syst. Group, Kokomo, IN, USA (Yuhui Shi) |
| Abstract | A co-evolutionary particle swarm optimization (PSO) to solve constrained optimization problems is proposed. First, we introduce the augmented Lagrangian to transform a constrained optimization to a min-max problem with the saddle-point solution. Next, a co-evolutionary PSO algorithm is developed with one PSO focusing on the minimum part of the min-max problem with the other PSO focusing on the maximum part of the min-max problem. The two PSOs are connected through the fitness function. In the fitness calculation of one PSO, the other PSO serves as the environment to that PSO. The new algorithm is tested on three benchmark functions. The simulation results illustrate the efficiency and effectiveness of the new co-evolutionary particle swarm algorithm. |
| Sponsorship | IEEE IEEE Neural Networks Soc. IEE Evolutionary Programming Soc |
| Starting Page | 1682 |
| Ending Page | 1687 |
| File Size | 452262 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780372824 |
| DOI | 10.1109/CEC.2002.1004495 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2002-05-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle swarm optimization Constraint optimization Lagrangian functions Sun Benchmark testing Evolutionary computation Decoding Genetic algorithms Sorting Annealing |
| Content Type | Text |
| Resource Type | Article |
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