Google Scholar (http://scholar.google.com/scholar_lookup? ... Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization ...
An enhanced symbiosis organisms search algorithm: an empirical study Neural Computing and Applications pp 1–19 Authors
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Salah Al-Sharhan, Mahamed G. H. Omran Original Article First Online: 20 October 2016 DOI (Digital Object Identifier): 10.1007/s00521-016-2624-x
Cite this article as: Al-Sharhan, S. & Omran, M.G.H. Neural Comput & Applic (2016). doi:10.1007/s00521-016-2624-x 44
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Abstract Many natureinspired optimization algorithms have recently been proposed to solve difficult optimization problems where the mathematical gradientbased approaches could not be used. However, those approaches were often not tested on a proper set of problems. Moreover, statistical tests are sometimes not used to validate the conclusions. Therefore, empirical analyses of such approaches are needed. In this paper, a very recent natureinspired approach, symbiosis organisms search (SOS), is investigated. A set of unbiased and characteristically different problems are used to study the performance of SOS. In addition, a comparison with some recent optimization methods is conducted. Then, the effect of SOS only parameter, eco_size, is studied, and the use of different random distributions is also explored. Finally, three simple SOS variants are proposed and compared to the original SOS. Conclusions are validated using nonparametric statistical tests.
Keywords
Symbiosis organisms search Evolutionary algorithms Natureinspired optimization algorithms and metaheuristics Salah AlSharhan and Mahamed G. H. Omran have contributed equally to this work.
References 1.
Cheng M, Prayogo D (2014) Symbiotic organism search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112 CrossRef (http://dx.doi.org/10.1016/j.compstruc.2014.03.007) Google Scholar (http://scholar.google.com/scholar_lookup?
title=Symbiotic%20organism%20search%3A%20a%20new%20metaheuristic%20optimization%20algorithm&author=M.%20Chen 112&publication_year=2014) 2.
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144 MathSciNet (http://www.ams.org/mathscinetgetitem?mr=3037522) CrossRef (http://dx.doi.org/10.1016/j.amc.2013.02.017) MATH (http://www.emis.de/MATHitem?1288.65092) Google Scholar (http://scholar.google.com/scholar_lookup?
title=Backtracking%20search%20optimization%20algorithm%20for%20numerical%20optimization%20problems&author=P.%20C 8144&publication_year=2013) 3.
Das S, Suganthan P (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical Report, Jadavpur University, Nanyang Technological University
4.
Garcia S, Molina D, Lozano M, Herrera F (2009) A study on the use of nonparametric
Support
tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644 CrossRef (http://dx.doi.org/10.1007/s1073200890804) MATH (http://www.emis.de/MATHitem?1191.68828) Google Scholar (http://scholar.google.com/scholar_lookup?title=A%20study%20on%20the%20use%20of%20non
parametric%20tests%20for%20analyzing%20the%20evolutionary%20algorithms%E2%80%99%20behaviour%3A%20a%20case%2 644&publication_year=2009) 5.
Jones D (2010) Good practice in (pseudo) random number generation for bioinformatics applications. Technical Report, UCL Bioinformatics Group
6.
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department
7.
Kennedy J (1999) Small worlds and megaminds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation, Washington DC, USA, pp 1931–1938
8.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international joint conference on neural networks. IEEE Press, pp. 1942–1948
9.
Matsumoto M, Nishimura T (1998) Mersenne twister: a 623dimensionally equidistributed uniform pseudorandom number generator. ACM Trans Model Comput Simul 8(1):3–30 CrossRef (http://dx.doi.org/10.1145/272991.272995) MATH (http://www.emis.de/MATHitem?0917.65005)
Google Scholar (http://scholar.google.com/scholar_lookup?title=Mersenne%20twister%3A%20a%20623dimensionally%20equid
random%20number%20generator&author=M.%20Matsumoto&author=T.%20Nishimura&journal=ACM%20Trans%20Model%20C 30&publication_year=1998) 10.
Peer E, Van den Bergh F, Engelbrecht A (2003) Using neighborhoods with the guaranteed convergence PSO. In: Swarm intelligence symposium, Piscataway, New Jersey, USA, IEEE Service Center, pp. 235–242
11.
Sandgren E (1990) Non linear integer and discrete programming in mechanical design optimization. J Mech Des 112(2):223–229 CrossRef (http://dx.doi.org/10.1115/1.2912596) Google Scholar (http://scholar.google.com/scholar_lookup?
title=Non%20linear%20integer%20and%20discrete%20programming%20in%20mechanical%20design%20optimization&author= 229&publication_year=1990) 12 .
Simon D (2013) Evolutionary optimization algorithms. Wiley, New York Google Scholar (http://scholar.google.com/scholar_lookup? title=Evolutionary%20optimization%20algorithms&author=D.%20Simon&publication_year=2013)
13 .
Sorensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22(1):3–18 MathSciNet (http://www.ams.org/mathscinetgetitem?mr=3298811) CrossRef (http://dx.doi.org/10.1111/itor.12001) MATH (http://www.emis.de/MATHitem?1309.90127) Google Scholar (http://scholar.google.com/scholar_lookup?
Support
title=Metaheuristics%E2%80%94the%20metaphor%20exposed&author=K.%20Sorensen&journal=Int%20Trans%20Oper%20Res 18&publication_year=2015) 14.
Sorensen K, Sevaux M, Glover F (2016) History of metaheuristics. Handbook of heuristics. Springer, New York Google Scholar (http://scholar.google.com/scholar_lookup?
title=History%20of%20metaheuristics.%20Handbook%20of%20heuristics&author=K.%20Sorensen&author=M.%20Sevaux&auth 15.
Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR95012, International Computer Science Institute, Berkeley, CA
16.
Suganthan P, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC2005 special session on realparameter optimization. Technical Report, Nanyang Technology University, Singapore
17.
Tang K, Yao X, Suganthan PN, MacNish C, Chen YP, Chen CM, Yang Z (2008) Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China
18.
Yang X (2012) Flower pollination algorithm for global optimization. Lect Notes Comput Sci 7445:240–249 CrossRef (http://dx.doi.org/10.1007/9783642328947_27) MATH (http://www.emis.de/MATHitem?06103583) Google Scholar (http://scholar.google.com/scholar_lookup?
title=Flower%20pollination%20algorithm%20for%20global%20optimization&author=X.%20Yang&journal=Lect%20Notes%20Com 249&publication_year=2012) 19.
Yang X (2014) Natureinspired optimization algorithms. Elsevier, Amsterdam MATH (http://www.emis.de/MATHitem?1291.90005) Google Scholar (http://scholar.google.com/scholar_lookup?title=Nature inspired%20optimization%20algorithms&author=X.%20Yang&publication_year=2014)
2 0.
Zhang J, Sanderson A (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958 CrossRef (http://dx.doi.org/10.1109/TEVC.2009.2014613) Google Scholar (http://scholar.google.com/scholar_lookup?
title=JADE%3A%20adaptive%20differential%20evolution%20with%20optional%20external%20archive&author=J.%20Zhang&au 958&publication_year=2009) 2 1.
Zhan Z, Zhan J, Li Y, Chung H (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B 39(6):1362–1381 CrossRef (http://dx.doi.org/10.1109/TSMCB.2009.2015956) Google Scholar (http://scholar.google.com/scholar_lookup?
title=Adaptive%20particle%20swarm%20optimization&author=Z.%20Zhan&author=J.%20Zhan&author=Y.%20Li&author=H.%2 1381&publication_year=2009) 22.
Zhou A, Sun J, Zhang Q (2015) An estimation of distribution algorithm with cheap and expensive local search methods. IEEE Trans Evol Comput 19(6):807–822 MathSciNet (http://www.ams.org/mathscinetgetitem?mr=3351822)
Support
CrossRef (http://dx.doi.org/10.1109/TEVC.2014.2387433) Google Scholar (http://scholar.google.com/scholar_lookup?
title=An%20estimation%20of%20distribution%20algorithm%20with%20cheap%20and%20expensive%20local%20search%20met 822&publication_year=2015)
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