Top

Memetic Computing

Published in:

10-06-2016 | Regular Research Paper

A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems

Authors: Sukanta Nama, Apu Kumar Saha, Sima Ghosh

Published in: Memetic Computing | Issue 3/2017

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this paper, a new hybrid algorithm, Hybrid Symbiosis Organisms Search (HSOS) has been proposed by combining Symbiosis Organisms Search (SOS) algorithm with Simple Quadratic Interpolation (SQI). The proposed algorithm provides more efficient behavior when dealing with real-world and large scale problems. To verify the performance of this suggested algorithm, 13 (Thirteen) well known benchmark functions, CEC2005 and CEC2010 special session on real-parameter optimization are being considered. The results obtained by the proposed method are compared with other state-of-the-art algorithms and it was observed that the suggested approach provides an effective and efficient solution in regards to the quality of the final result as well as the convergence rate. Moreover, the effect of the common controlling parameters of the algorithm, viz. population size, number of fitness evaluations (number of generations) of the algorithm are also being investigated by considering different population sizes and the number of fitness evaluations (number of generations). Finally, the method endorsed in this paper has been applied to two real life problems and it was inferred that the output of the proposed algorithm is satisfactory.

previous article Combined separable signals based neuro-fuzzy Hammerstein–Wiener model

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Cheng MY, Prayogo D (2014) Symbiotic Organisms Search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRef

Das S, Suganthan PN (2010) Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems, Technical Report, December. http://www.ntu.edu.sg/home/EPNSugan

Gao W-f, Liu S-y, Huang L-l (2012) Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. Commun Nonlinear Sci Numer Simulat 17:4316–4327MathSciNetCrossRefMATH

Gong W, Cai Z, Ling CX (2011) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15:645–665CrossRef

Holland JH (1992) Adaptation in natural and artificial systems. University of Michigan Press. ISBN: 0-262-58111-6

Marinaki M, Marinakis Y (2015) A hybridization of clonal selection algorithm with iterated local search and variable neighborhood search for the feature selection problem. Memetic Comput 7:181–201CrossRef

Osman IH, Laporte G (1996) Metaheuristics: a bibliography. Ann Oper Res 63:513–623CrossRefMATH

Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(5):702–713CrossRef

Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359MathSciNetCrossRefMATH

10.

Tsai HC (2015) Roach infestation optimization with friendship centers. Eng Appl Artif Intell 39:109–119CrossRef

11.

Sorensen K (2015) Metaheuristics–the metaphor exposed. Int Trans Oper Res 22:3–18MathSciNetCrossRefMATH

12.

Crepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surveys 45(3):35MATH

13.

Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Nanyang Tech. Univ., Singapore and KanGAL, Kanpur Genetic Algorithms Lab., IIT, Kanpur, India, Tech. Rep., Rep. No. 2005005, May 2005

14.

Rahnamayan S, Tizhoosh H, Salama M (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79CrossRef

15.

Bhattacharjee K, Bhattacharya A, Nee Dey SH (2015) Backtracking search optimization based economic environmental power dispatch problems. Electr Power Energy Syst 73:830–842CrossRef

16.

Storn R (1996) On the usage of differential evolution for function optimization”, in: Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, Berkeley, pp 519–523

17.

Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11:1679–1696CrossRef

18.

Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295CrossRef

19.

Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195CrossRef

20.

Tanweer MR, Suresha S, Sundararajan N (2015) Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems. Inf Sci. doi:10.1016/j.ins.2015.07.035

21.

Ong YS, Lim MH, Chen XS (2010) Research frontier: memetic computation—past, present & future. IEEE Comput Intell Mag 5(2):24–36CrossRef

22.

Deep K, Das KN (2008) Quadratic approximation based hybrid genetic algorithm for function optimization. Appl Math Comput 203(1):86–98MATH

23.

Tang K, Li X, Suganthan PN, Yang Z, Weise T (2010) Benchmark Functions for the CEC’2010 Special Session and Competition on Large-Scale Global Optimization. July 8, 2010

24.

Wanga Y, Cai Z, Zhang Q (2012) Enhancing the search ability of differential evolution through orthogonal crossover. Inf Sci 185:153–177MathSciNetCrossRef

25.

Parsopoulos KE, Vrahatis MN (2004) UPSO-A unified particle swarm optimization scheme. Lect Ser Comput Sci 1:868–873

26.

Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8:204–210CrossRef

27.

van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8:225–239CrossRef

28.

Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73CrossRef

29.

Kennedy J (2003) Bare bones particle swarms. In: Proceedings of the IEEE SIS, pp 80–87

30.

Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings of the IEEE SIS, pp 210–224

31.

Mo H, Liu L, Xu L (2014) A power spectrum optimization algorithm inspired by magnetotactic bacteria. Neural Compt Appl 25(7):1823–1844CrossRef

32.

Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471MathSciNetCrossRefMATH

33.

Chen X, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 15(5):591–607CrossRef

34.

Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: IEEE International Conference on computational intelligence, pp 69–73

35.

Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: IEEE international conference evolutionary computation, Honolulu, HI, pp 1671–1676

36.

Kennedy J, Mendes R (2006) Neighborhood topologies in fully informed and best-of neighborhood particle swarms. IEEE Trans Syst Man Cybern Part C 36(4):515–9

37.

Zhan ZH, Zhang J, Li Y, Chung HH (2009) Adaptive particle swarm optimization. IEEE Trans B 39(6):1362–1381

38.

Pan I, Das S (2013) Design of hybrid regrouping PSO-GA based sub-optimal networked control system with random packet losses. Memetic Compt 5:141–153CrossRef

Title: A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems
Authors: Sukanta Nama
Apu Kumar Saha
Sima Ghosh
Publication date: 10-06-2016
Publisher: Springer Berlin Heidelberg
Published in: Memetic Computing / Issue 3/2017
Print ISSN: 1865-9284
Electronic ISSN: 1865-9292
DOI: https://doi.org/10.1007/s12293-016-0194-1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2017

Combined separable signals based neuro-fuzzy Hammerstein–Wiener model

An efficient parallel method for batched OS-ELM training using MapReduce

Denoising deep extreme learning machine for sparse representation

Editorial

A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method

An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

Premium Partner