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IJSTR >> Volume 5 - Issue 7, July 2016 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Evolutionary Algorithms Performance Comparison For Optimizing Unimodal And Multimodal Test Functions

[Full Text]

 

AUTHOR(S)

Dr. Hanan A.R. Akkar, Firas R. Mahdi

 

KEYWORDS

Benchmark test functions, Evolutionary population based algorithms, Meta-heuristic techniques, Optimization.

 

ABSTRACT

Many evolutionary algorithms have been presented in the last few decades, some of these algorithms were sufficiently tested and used in many researches and papers, such as: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution Algorithm (DEA). Other recently proposed algorithms were unknown and rarely used such as Stochastic Fractal Search (SFS), Symbiotic Organisms Search (SOS), and Grey Wolf Optimizer (GWO). This paper trying to made a fair comprehensive comparison for the performance of these well-known algorithms and other less prevalent and recently proposed algorithms, by using a variety of famous test functions that have multiple different characteristics, through applying two experiments for each algorithm according to the used test function, the first experiments carried out with the standard search space limits of the proposed test functions, while the second experiment multiple ten times the maximum and minimum limits of the test functions search space, recording the Average Mean Absolute Error (AMAE), Overall Algorithm Efficiency (OAE), Algorithms Stability (AS), Overall Algorithm Stability (OAS), each algorithm required Average Processing Time (APT), and Overall successful optimized test function Processing Time (OPT) for both of the experiments, and with ten epochs each with 100 iterations for each algorithm.

 

REFERENCES

[1] Alazzam, and H. W. Lewis, "A New Optimization Algorithm For Combinatorial Problems", Int. J. of Advanced Research in Artificial Intelligence, vol. 2, no.5, pp. 63-68, 2013.

[2] J. F. Frenzel, "Genetic algorithms," in IEEE Potentials, vol. 12, no. 3, pp. 21-24, Oct. 1993. doi: 10.1109/45.282292

[3] J. Kennedy and R. Eberhart, "Particle swarm optimization," Neural Networks, 1995. Proceedings., IEEE International Conference on, Perth, WA, 1995, pp. 1942 1948 vol.4. doi: 10.1109/ICNN.1995.488968

[4] D. Karaboga, and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", Journal of Global Optimization, vol. 39, no.3, pp. 459-471, 2007.

[5] H. Salimi, "Stochastic Fractal Search: A powerful meta-heuristic algorithm", Elsevier Science Ltd, Knowledge-Based Systems, vol. 75, pp. 1-18, 2015.

[6] M. Cheng, and D. Prayogo, "Symbiotic Organisms Search: A new metaheuristic optimization algorithm", Elsevier Science Ltd, Computers & Structures, vol. 139, pp. 98–112, 2014.

[7] S. Mirjalilia, S. M. Mirjalilib, and A. Lewisa, "Grey Wolf Optimizer", Elsevier Science Ltd, Advances in Engineering Software, vol. 69, 46-61, 2014.

[8] X. Menga, X.Z. Gaob, Y. Liuc, and H. Zhanga, " A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization", Elsevier Science Ltd, Expert Systems with Applications, vol. 42, no. 17–18, pp. 6350–6364, 2015.

[9] Z. Beheshti, S. Mariyam, and H. Shamsuddin," A Review of Population-based Meta-heuristic Algorithms", International Journal of Advances in Soft Computing and its Application. vol.5, no.1, 2013.

[10] X. Menga, X.Z. Gao, L. Lu, Y. Liu, and H. Zhang, "A new bio-inspired optimization algorithm: Bird Swarm Algorithm", Taylor & Francis, Experimental & Theoretical Artificial Intelligence. 2015.

[11] X. S. Yang and Suash Deb, "Cuckoo Search via Lévy flights," Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, Coimbatore , pp. 210-214. 2009. doi: 10.1109/NABIC.2009.5393690

[12] X. Meng, Y. Liu, X. Gao, and H. Zhang, " A new bio-inspired algorithm: Chicken Swarm Optimization", proc. ICSI, Part I, LNCS 8794,pp. 86-94, 2014.

[13] S. Mirjalili, " Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete and multi-objective problems", Springer, Neural Computing and Applications, pp.1-21, 2015.

[14] X. Yang, M. Karamanoglu, and X. He, " Multi-objective Flower Algorithm for Optimization”, Int. Conf., Computational Science, Proc. Of Computer Science, vol. 18, pp. 861 – 868, 2013.

[15] E. Rashedi, H. Nezamabadi-pour & S. Saryazdi, " GSA: A Gravitational Search Algorithm", Elsevier Science Ltd, Information Sciences, vol. 179, pp. 2232–2248, 2009.

[16] J. Yang and J. Zhu, "A Modified Harmony Search Algorithm for Optimization Problems," Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on, Hangzhou, pp. 100-104, 2012. doi: 10.1109/ISCID.2012.177.

[17] S. Mirjalili, "Moth-Flame Optimization Algorithm: A Novel Nature-inspired Heuristic Paradigm", Elsevier Science Ltd. , Knowledge-Based Systems, vol. 89, pp. 228–249, 2015.

[18] S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, " Multi-Verse Optimizer: a nature-inspired algorithm for global optimization", Springer Neural Computing and Applications, vol. 27, no.2, pp. 495-513, 2016.

[19] Shuyuan Yang, Min Wang and Licheng jiao, "A quantum particle swarm optimization," Evolutionary Computation, 2004. CEC2004. Congress on, pp. 320-324, vol.1. 2004. doi: 10.1109/CEC.2004.1330874

[20] E. Cuevas, A. Echavarría, and M. A. Ramírez-Ortegón,"An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation", Springer, Applied Intelligence, vol. 40, no.2, pp. 256-272, 2014.

[21] Z. Bayraktar, M. Komurcu and D. H. Werner, "Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics," 2010 IEEE Antennas and Propagation Society International Symposium, Toronto, ON, 2010, pp. 1-4. doi: 10.1109/APS.2010.5562213

[22] M. Jamil, and X.Yang," A literature survey of benchmark functions for global optimization problems", Int. Journal of Mathematical Modelling and Numerical Optimisation, vol. 4, no.2, pp.150–194, 2013.

[23] X.S. Yang, (2010). Appendix A Test Problems in Optimization, in Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiely & Sons, Inc, 1st ed., USA, 261-166.

[24] H. Pohlheim, "Examples of Objective Functions", Genetic and Evolutionary Algorithm Toolbox for Matlab (GEATbx). version 3.8, [online], Available: http://www.geatbx.com/download/GEATbx_ObjFunExpl_v38.pdf. 2006