[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Variations of cohort intelligence

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

A cohort refers to a group of candidates interacting and competing with one another. The basic idea of cohort is inspired from the social tendency of following/learning from one another and adapting the qualities of certain candidate. Based upon this approach, seven variations of cohort intelligence (CI) are presented in this paper. The seven variations of CI are: follow best, follow better, follow worst, follow itself, follow median, follow roulette wheel selection and alienate-and-random selection. The proposed variations are tested on seven multimodal and three uni-modal unconstrained test functions, and the numerical results are analyzed to decide which variation works best for a particular type of problem. The performance of these variations is compared with some well-known algorithms namely PSO, CMAES, ABC, JDE, CLPSO, SADE and BSA. The analysis of variations gives very important insight about the strategy that should be followed while working in a cohort. The variations proposed may provide insight into variegated applicability domain of the CI methodology. The choice of the right variation may also further open doors for CI to solve different real-world problems.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Bonilla-Petriciolet A, Segovia-Hernández JG (2009) Particle swarm optimization for phase stability and equilibrium calculations in reactive systems. Comput Aided Chem Eng 26:635–640

    Article  Google Scholar 

  • Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. In 2007 IEEE swarm intelligence symposium. IEEE, pp 120–127

  • Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):646–657

    Article  Google Scholar 

  • Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  • Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346

    Article  Google Scholar 

  • Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2):311–338

    Article  MATH  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B 26(1):29–41

    Article  Google Scholar 

  • Huan TT, Kulkarni AJ, Kanesan J, Huang CJ, Abraham A (2016) Ideology algorithm: a socio-inspired optimization methodology. Neural Comput Appl. doi:10.1007/s00521-016-2379-4

  • Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4(2):150–194

    MATH  Google Scholar 

  • Kanzow C, Yamashita N, Fukushima M (2005) Levenberg–Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints. J Comput Appl Math 173(2):321–343

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  • Kulkarni AJ, Tai K (2010) Probability collectives: a multi-agent approach for solving combinatorial optimization problems. Appl Soft Comput 10(3):759–771

    Article  Google Scholar 

  • Kulkarni AJ, Tai K (2013) A probability collectives approach for multi-agent distributed and cooperative optimization with tolerance for agent failure. In: Czarnowski I, Jedrzejowicz P, Kacprzyk J (eds) Agent based optimization, studies in computational intelligence, vol 456. Springer, pp 175–201

  • Kulkarni AJ, Shabir H (2016) Solving 0–1 knapsack problem using cohort intelligence algorithm. Int J Mach Learn Cybern 7(3):427–441

    Article  Google Scholar 

  • Kulkarni AJ, Patankar NS, Sandupatla A, Tai K (2012a) A modified feasibility-based rule for solving constrained optimization problems using Probability Collectives. In: 2012 12th International conference on hybrid intelligent systems (HIS), pp 213–218

  • Kulkarni AJ, Kale IR, Tai K, Azad SK (2012b) Discrete optimization of truss structure using probability collectives. In: 2012 12th International conference on hybrid intelligent systems (HIS), pp 225–230

  • Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self supervised learning behavior. In: 2013 IEEE international conference on systems, man, and cybernetics, pp 1396–1400

  • Krishnasamy G, Kulkarni AJ, Paramesran R (2014) A hybrid approach for data clustering based on modified cohort intelligence and K-means. Expert Syst Appl 41(13):6009–6016

    Article  Google Scholar 

  • Kulkarni AJ, Baki MF, Chaouch BA (2016) Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems. Eur J Oper Res 250(2):427–447

    Article  MathSciNet  MATH  Google Scholar 

  • Kulkarni AJ, Krishnasamy G, Abraham A (2017) Cohort intelligence: a socio-inspired optimization method. Intelligent Systems Reference Library, vol 114. Springer. doi:10.1007/978-3-319-44254-9 (ISBN: 978-3-319-44254-9)

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

    Article  Google Scholar 

  • Liu B, Wang L, Liu Y, Qian B, Jin YH (2010) An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes. Comput Chem Eng 34(4):518–528

    Article  Google Scholar 

  • Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1–2):61–106

    Article  Google Scholar 

  • Omran MGH, Clerc M (2011) http://www.particleswarm.info/. Accessed 27 Aug 2016

  • Patankar NS, Kulkarni AJ, Tai K, Ghate TD, Parvate AR (2014) Multi-criteria probability collectives. Int J Bio-Inspired Comput 6(6):369–383

    Article  Google Scholar 

  • Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2011) The bees algorithm—a novel tool for complex optimization. In: Intelligent production machines and systems—2nd I* PROMS virtual international conference, 3–14 July 2006

  • Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE congress on evolutionary computation. IEEE, vol 2, pp 1785–1791

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  • Ray T, TAI K, SEOW KC (2001) Multiobjective design optimization by an evolutionary algorithm. Eng Optim 33(4):399–424

    Article  Google Scholar 

  • Shi Y (2014) Emerging research on swarm intelligence and algorithm optimization. IGI Publishing Hershey, PA, USA

  • Thangaraj R, Pant M, Abraham A, Bouvry P (2011) Particle swarm optimization: hybridization perspectives and experimental illustrations. Appl Math Comput 217(12):5208–5226

    MATH  Google Scholar 

  • Tsoulos IG, Stavrakoudis A (2010) Enhancing PSO methods for global optimization. Appl Math Comput 216(10):2988–3001

    MathSciNet  MATH  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Operations Research, North Carolina State University, Raleigh, NC, 27606, USA

    N. S. Patankar

  2. Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 3P4, Canada

    Anand J. Kulkarni

  3. Department of Mechanical Engineering, Symbiosis Institute of Technology, Symbiosis International University, Lavale, Pune, Maharashtra, 412 115, India

    Anand J. Kulkarni

Authors
  1. N. S. Patankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anand J. Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand J. Kulkarni.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by A. Di Nola.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patankar, N.S., Kulkarni, A.J. Variations of cohort intelligence. Soft Comput 22, 1731–1747 (2018). https://doi.org/10.1007/s00500-017-2647-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-017-2647-y

Keywords

Search

Navigation