Skip to main content
SpringerLink
Log in

Troop search optimization algorithm for constrained problems

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Troop search optimization (TSO) algorithm is motivated by the dynamic controls of commander/captain over the troops before (during) each combat operation to concoct (active) a bravery battalion force. The attempt is focused to maintain the proper balance between exploration and exploitation in the search space during simulation. The inclusion of operators like ‘swapping crossover’ and ‘cut and fill’ provides additional features in TSO algorithm to make it more robust. The efficiency of TSO is tested over a set of constrained optimization problem test suite CEC 2010. Apart from that, TSO is also employed to solve five real life constrained engineering optimization problems. The empirical results, comparative statistical and graphical analysis concludes with the superiority of TSO over the state-of-art algorithms in solving constrained optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23:1001–1014

    Article  Google Scholar 

  • Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287

    Article  MathSciNet  MATH  Google Scholar 

  • Das KN, Singh TK (2014) Drosophila food-search optimization. Appl Math Comput 231:566–580

    MathSciNet  MATH  Google Scholar 

  • Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338

    Article  MATH  Google Scholar 

  • Deb K (2010) Optimization for engineering design algorithms and examples. Prentice Hall of India, New Delhi

    Google Scholar 

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

    MATH  Google Scholar 

  • Floudas C.A, Pardalos PM (1987) A collection of test problems for constrained global optimization algorithms. In: Goos G, Hartmanis J (eds) LNCS, vol 455. Springer, Newyork

  • He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99

    Article  Google Scholar 

  • Ho PY, Shimizu K (2007) Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme. Inf Sci 177:2985–3004

    Article  Google Scholar 

  • Homaifar A, Lai AHY, Qi X (1994) Constrained optimization via genetic algorithms. Simulation 2(4):242–254

    Article  Google Scholar 

  • Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186:340–356

    MathSciNet  MATH  Google Scholar 

  • Jiao LC, Li L, Shang RH, Liu F, Stolkin R (2013) A novel selection evolutionary strategy for constrained optimization. Inf Sci 239:122–141

    Article  MathSciNet  Google Scholar 

  • Joines JA, Houck CR (1994) On the use of non stationary penalty functions to solve nonlinear constrained optimization problems with GA’s. In: Proceedings of IEEE international conference on evolutionary computation, pp 579–585

  • Juarez-Castillo E, Perez-Castro N, Mezura-Montes E (2015) A novel boundary constraint-handling technique for constrained numerical optimization problems. In: IEEE congress on evolutionary computation (CEC) pp 2034–2041

  • Koziel S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol Comput 7:19–44

    Article  Google Scholar 

  • LaTorre A, Muelas S, Pena JM (2015) A comprehensive comparison of large scale global optimizers. Inf Sci 316:517–549

    Article  Google Scholar 

  • Lemonge ACC, Barbosa HJC (2004) Adaptive penalty scheme for genetic algorithms in structural optimization. Int J Numer Methods in Eng 59:703–736

    Article  MATH  Google Scholar 

  • Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640

    Article  Google Scholar 

  • Liu L, Mu H, Yang J (2015) Generic constraints handling techniques in constrained multi-criteria optimization and its application. Eur J Oper Res 244(2):576–591

    Article  MathSciNet  MATH  Google Scholar 

  • Mallipeddi R, Suganthan PN (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14:561–579

    Article  Google Scholar 

  • Mezura-Montes E, Coello CAC (2005) Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: Gelbukh A, de Albornoz A, Terashima-Marin H (eds) MICAI 2005 advances in artificial intelligence; lecture notes in computer science, vol 3789. Springer, Berlin, Heidelberg, pp 652–662

  • Michalewicz Z, Schoenauer M (1995) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32

    Article  Google Scholar 

  • Mohammad AE, Shoukry AA (2014) Constrained dynamic differential evolution using a novel hybrid constraint handling technique. In: IEEE international conference on systems, man, and cybernetics (SMC) pp 2421–2426

  • Mohan C, Shankar K (1994) A random search technique for global optimization based on quadratic approximation. Asia Pac J Oper Res 11:93–101

    Google Scholar 

  • Panda R, Agrawal S, Sahoo M, Nayak R (2016) A novel evolutionary rigid body docking algorithm for medical image registration. Swarm Evol Comput. doi:10.1016/j.swevo.2016.11.002

    Google Scholar 

  • Parsopoulos KE, Vrahatis MN (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. Adv Nat Comput Lect Notes Comput Sci 3612:582–591

    Google Scholar 

  • Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. CAD 43:303–315

    Google Scholar 

  • Rodrigues MC, Lima BSLP, Guimaraes S (2016) Balanced ranking method for constrained optimization problems using evolutionary algorithms. Inf Sci 327:71–90

    Article  Google Scholar 

  • Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4(3):274–283

    Article  Google Scholar 

  • Runarsson TP, Yao X (2002) Constrained evolutionary optimization: the penalty function approach, Chapter 4. In: Sarker R, Mohammadian M, Yao X (eds) Evolutionary optimization. Kluwer Academic Publishers, Dordrecht, pp 87–113. ISBN 0-7923-7654-4

    Google Scholar 

  • Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine bomb algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612

    Article  Google Scholar 

  • Saha C, Das S, Pal K, Mukherjee S (2014) A fuzzy rule-based penalty function approach for constrained evolutionary optimization. IEEE Trans Cybern 46(12):2953–2965

    Article  Google Scholar 

  • Yang JM, Chen YP, Horng JT, Kao CY (1997) Applying family competition to evolution strategies for constrained optimization, vol 1213. Lecture notes in computer Science. Springer, Berlin, pp 201–211

    Google Scholar 

  • Zaman MF, Saber ME, Ray T, Sarker RA (2016) Evolutionary algorithms for dynamic economic dispatch problems. IEEE Trans Power Syst 31(2):1486–1495

    Article  Google Scholar 

  • Zhang H, Ishikawa M (2004) An extended hybrid genetic algorithm for exploring a large search space. In: 2nd International conference on autonomous robots and agents, Palmerston North, New Zealand, pp 244–248

  • Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178:3043–3074

    Article  Google Scholar 

  • Zhang G, Cheng J, Gheorghe M, Meng Q (2013) A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems. Appl Soft Comput 13:1528–1542

    Article  Google Scholar 

  • Zhang C, Lin Q, Gao L, Li X (2015) Backtracking search algorithm with three constraint handling methods for constrained optimization problems. Expert Syst Appl 42(21):7831–7845

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology, Silchar, 788010, India

    Biplab Chaudhuri & Kedar Nath Das

Authors
  1. Biplab Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kedar Nath Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Biplab Chaudhuri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaudhuri, B., Das, K.N. Troop search optimization algorithm for constrained problems. Int J Syst Assur Eng Manag 9, 755–773 (2018). https://doi.org/10.1007/s13198-017-0640-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-017-0640-6

Keywords

Search

Navigation