A Look-Ahead Based Meta-Heuristics for Optimizing Continuous Optimization Problems
Chapter, Peer reviewed
Accepted version
Permanent lenke
https://hdl.handle.net/11250/2836508Utgivelsesdato
2022Metadata
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Originalversjon
Nordli, T., & Bouhmala, N. (2022). A Look-Ahead Based Meta-Heuristics for Optimizing Continuous Optimization Problems. I International Conference on Optimization, Learning Algorithms and Applications (s. 48-55). Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_4Sammendrag
In this paper, the famous kernighan-Lin algorithm is adjusted and embedded into the simulated annealing algorithm and the genetic algorithm for continuous optimization problems. The performance of the different algorithms are evaluated using a set of well known optimization test functions.