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Benchmarking Meta-Heuristic Optimization


Affiliations
1 Department of Information Systems, Helwan University - Cairo, Egypt
2 Department of Computer Science, Helwan University - Cairo, Egypt
 

Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm’s result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.

Keywords

Optimization, Algorithms, Benchmark.
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  • Hoos H., Stützle, T., Stochastic local search: Foundations and applications. Elsevier, 2004.

  • Dixon L., Szegö G.,Towards global optimisation. University of Cagliari, Italy, October 1974. Amsterdam-Oxford. North-Holland Publ. Co. 1975. X. 472 S”. In: ZeitschriftAngewandteMathematik und Mechanik59 (1979), pp. 137–138.

  • Beal, P. “A Study of Genetic Algorithm Techniques for the Design of Metamaterial Ferrites”. PhD thesis. Queen Mary University of London, 2019.

  • Abuiziah I., Shakarneh N. “A Review of Genetic Algorithm Optimization: Operations and Applications to Water Pipeline Systems”. In: International Journal of Physical, Natural Science and Engineering 7 (Dec. 2013).

  • Kang-Ping Wang et al. “Particle swarm optimization for traveling salesman problem”. In: Proceedings of the 2003 international conference on machine learning and cybernetics (IEEE cat. no. 03ex693). Vol. 3. IEEE. 2003, pp. 1583–1585.

  • Eberhart R.,Shi. Y. “Comparison between genetic algorithms and particle swarm optimization”. In: International conference on evolutionary programming. Springer. 1998, pp. 611–616.

  • Mahmud Iwan et al. “Performance comparison of differential evolution and particle swarm optimization in constrained optimization”. In: Procedia Engineering 41 (2012), pp. 1323–1328.

  • Mirjalili S., Lewis A. “Grey wolf optimizer”. In: Advances in engineering software 69 (2014), pp. 46–61.

  • Panda M., Das. B., “Grey Wolf Optimizer and Its Applications: A Survey”. In: Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Springer. 2019, pp. 179–194.

  • Lampinen J.,Storn R., “Differential evolution”. In: New optimization techniques in engineering. Springer, 2004, pp. 123–166.

  • Kirkpatrick S., Gelatt, Vecchi M., “Optimization by simulated annealing”. In: science 220.4598 (1983), pp. 671–680.

  • Vanderbilt D., Louie S., “A Monte Carlo simulated annealing approach to optimization over continuous variables”. In: Journal of computational physics 56.2 (1984), pp. 259–271.

  • Mahdi W., Medjahed S., and Ouali M., “Performance analysis of simulated annealing cooling schedules in the context of dense image matching”. In: Computación y Sistemas21.3 (2017), pp. 493–501.

  • Chong E., and Zak S. An introduction to optimization. John Wiley & Sons, 2004.

  • Eberhart R., and Shi Y., “Comparison between Genetic Algorithms and Particle Swarm Optimization.” In: vol. 1447. Mar. 1998, pp. 611– 616.

  • Singh D., Khare A., Different Aspects of Evolutionary Algorithms, Multi-Objective Optimization Algorithms and Application Domain, Int. J. Advanced Networking and Applications, Volume: 02, Issue: 04, Pages: 770-775 (2011).


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  • Benchmarking Meta-Heuristic Optimization

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Authors

Mona Nasr
Department of Information Systems, Helwan University - Cairo, Egypt
Omar Farouk
Department of Computer Science, Helwan University - Cairo, Egypt
Ahmed Mohamedeen
Department of Computer Science, Helwan University - Cairo, Egypt
Ali Elrafie
Department of Computer Science, Helwan University - Cairo, Egypt
Marwan Bedeir
Department of Computer Science, Helwan University - Cairo, Egypt
Ali Khaled
Department of Computer Science, Helwan University - Cairo, Egypt

Abstract


Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm’s result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.

Keywords


Optimization, Algorithms, Benchmark.

References