AMA: a new approach for solving constrained real-valued optimization problems
| Autor: | Abu S. S. M. Barkat Ullah, Chris Lokan, David Cornforth, Ruhul A. Sarker |
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| Rok vydání: | 2008 |
| Předmět: |
Mathematical optimization
Optimization problem business.industry Crossover Evolutionary algorithm Constrained optimization Computational intelligence Theoretical Computer Science Memetic algorithm Local search (optimization) Geometry and Topology Artificial intelligence business Metaheuristic Software Mathematics |
| Zdroj: | Soft Computing. 13:741-762 |
| ISSN: | 1433-7479 1432-7643 |
| DOI: | 10.1007/s00500-008-0349-1 |
| Popis: | Memetic algorithms (MA) have recently been applied successfully to solve decision and optimization problems. However, selecting a suitable local search technique remains a critical issue of MA, as this significantly affects the performance of the algorithms. This paper presents a new agent based memetic algorithm (AMA) for solving constrained real-valued optimization problems, where the agents have the ability to independently select a suitable local search technique (LST) from our designed set. Each agent represents a candidate solution of the optimization problem and tries to improve its solution through co-operation with other agents. Evolutionary operators consist of only crossover and one of the self-adaptively selected LSTs. The performance of the proposed algorithm is tested on five new benchmark problems along with 13 existing well-known problems, and the experimental results show convincing performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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