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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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Author Biographies

Tirana Noor Fatyanosa, Universitas Brawijaya

Faculty of Computer Science

Wayan Firdaus Mahmudy, Universitas Brawijaya

Faculty of Computer Science
How to Cite
Fatyanosa, T. N., Sihananto, A. N., Alfarisy, G. A. F., Burhan, M. S., & Mahmudy, W. F. (2017). Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization. Journal of Information Technology and Computer Science, 1(2), 82–97. https://doi.org/10.25126/jitecs.20161215
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References

  1. Liu X, Jiao X, Li C, Huang M (2013) Research of Job-Shop Scheduling Problem Based on Improved Crossover Strategy Genetic Algorithm. In: Proc. 2013 3rd Int. Conf. Comput. Sci. Netw. Technol. pp 1–4
  2. Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge
  3. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, New York
  4. Kirkpatrick S, Gelatt CD, Vecchi MP (2007) Optimization by Simulated Annealing. Science (80- ) 220:671–680. doi: 10.1126/science.220.4598.671
  5. Junghans L, Darde N (2015) Hybrid single objective genetic algorithm coupled with the simulated annealing optimization method for building optimization. Energy Build 86:651–662. doi: 10.1016/j.enbuild.2014.10.039
  6. Mahmudy WF (2014) Improved simulated annealing for optimization of vehicle routing problem with time windows ( VRPTW ). Kursor 7:109–116.
  7. Liu W, Ye J (2014) Collapse optimization for domes under earthquake using a genetic simulated annealing algorithm. J Constr Steel Res 97:59–68. doi: 10.1016/j.jcsr.2014.01.015
  8. Chen PH, Shahandashti SM (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom Constr 18:434–443. doi: 10.1016/j.autcon.2008.10.007
  9. Deb K (2001) Multi-objective Optimization using Evolutionary Algorithms. Wiley, Chichester, United Kingdom
  10. Vasan A (2005) Studies on advanced modeling techniques for optimal reservoir operation and performance evaluation of an irrigation system. Birla Institute of Technology and Science, Pilani, India
  11. Brownlee J (2011) Clever Algorithms: Nature-Inspired Programming Recipes, 2nd ed.
  12. Al-Khateeb B, Tareq WZ (2013) Solving 8-Queens Problem by Using Genetic Algorithms, Simulated Annealing, and Randomization Method. In: Int. Conf. Dev. eSystems Eng. pp 187–191
  13. Vasan A, Raju KS (2009) Comparative analysis of Simulated Annealing, Simulated Quenching and Genetic Algorithms for optimal reservoir operation. Appl Soft Comput 9:274–281.
  14. Crossland AF, Jones D, Wade NS (2014) Electrical Power and Energy Systems Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing. Int J Electr Power Energy Syst 59:103–110. doi: 10.1016/j.ijepes.2014.02.001
  15. Czapinski M (2010) Parallel Simulated Annealing with Genetic Enhancement for flowshop problem. Comput Ind Eng 59:778–785. doi: 10.1016/j.cie.2010.08.003
  16. Mahmudy W, Marian R, Luong L (2013) Hybrid Genetic Algorithms for Multi-Period Part Type Selection and Machine Loading Problems in Flexible Manufacturing System. In: IEEE Int. Conf. Comput. Intell. Cybern. pp 126–130
  17. Jamil M, Yang X-S (2013) A Literature Survey of Benchmark Functions For Global Optimization Problems Citation details: Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4:150–194. doi: 10.1504/IJMMNO.2013.055204
  18. Oltean M (2003) Evolving Evolutionary Algorithms for Function Optimization. 7th Jt Conf Inf Sci 1:295–298.
  19. Pehlivanoglu YV (2013) A New Particle Swarm Optimization Method Enhanced With a Periodic Mutation Strategy. 17:436–452.
  20. Pant M, Thangaraj R, Abraham A (2009) Particle Swarm Optimization : Performance Tuning and Empirical Analysis. Foundations 3:101–128. doi: 10.1007/978-3-642-01085-9