An augmented lagrangian fish swarm based method for global optimization
This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A...
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| Other Authors: | , |
| Format: | article |
| Language: | eng |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1822/12909 |
| Country: | Portugal |
| Oai: | oai:repositorium.sdum.uminho.pt:1822/12909 |
| Summary: | This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical results with a benchmark set of problems are shown, including a comparison with other stochastic-type algorithms. |
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