Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization
Journal of Information Technology and Computer Science2017Vol. 1(2), pp. 82–82
Citations Over TimeTop 21% of 2017 papers
Tirana Noor Fatyanosa, Andreas Nugroho Sihananto, Gusti Ahmad Fanshuri Alfarisy, M.Shochibul Burhan, Wayan Firdaus Mahmudy
Abstract
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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