Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization
The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Anneali...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Brawijaya
2017-02-01
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| Series: | JITeCS (Journal of Information Technology and Computer Science) |
| Online Access: | http://jitecs.ub.ac.id/index.php/jitecs/article/view/15 |
| Summary: | The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result |
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| ISSN: | 2540-9433 2540-9824 |