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Proceedings of Fifth International Conference on Soft Computing for Problem Solving pp 839–850Cite as

Investigation of Suitable Perturbation Rate Scheme for Spider Monkey Optimization Algorithm

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

Abstract

Spider Monkey Optimization (SMO) is a new metaheuristic whose strengths and limitations are yet to be explored by the research community. In this paper, we make a small but hopefully significant effort in this direction by studying the behaviour of SMO under varying perturbation rate schemes. Four versions of SMO are proposed corresponding to constant, random, linearly increasing and linearly decreasing perturbation rate variation strategies. This paper aims at studying the behaviour of SMO technique by incorporating these different perturbation rate variation schemes and to examine which scheme is preferable to others on the benchmark set of problems considered in this paper. A benchmark set of 15 unconstrained scalable problems of different complexities including unimodal, multimodal, discontinuous, etc., serves the purpose of studying this behaviour. Not only numerical results of four proposed versions have been presented, but also the significance in the difference of their results has been verified by a statistical test.

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Abbreviations

Dim:

No. of dimensions

R(a, b):

Uniformly generated random number between a and b

NG:

Number of groups in current swarm

Pr:

Perturbation rate

G[k]:

Number of members in the kth group

I[k][0]:

Index of first member of the kth group in the swarm

I[k][1]:

Index of last member of the kth group in the swarm

S i :

Position vector of ith spider monkey in the swarm

s ij :

jth coordinate of the position of ith spider monkey

S new :

A trial vector for creating a new position of a spider monkey

S r :

Position vector of randomly selected member of group

ll kj :

jth co-ordinate of the local leader of the kth group

gl j :

jth co-ordinate of the global leader of the swarm

s minj :

Lower bound on the jth decision variable

s maxj :

Upper bound on the jth decision variable

fitness (S i ):

Fitness of the position of ith spider monkey

LLC k :

Limit count of the local leader of the kth group

References

  1. Gonga, A., Tayal, A.: Metaheuristics: review and application. J. Exp. Theor. Artif. Intell. 25(4), 503–526 (2013)

    Article  Google Scholar 

  2. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan press, AnnArbor (1975)

    Google Scholar 

  3. Dorigo, M.: Optimization, Learning and Natural Algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  4. Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. 4, 1942–1948 (1995)

    Google Scholar 

  5. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  6. Karaboga, D.: An idea based on honeybee swarm for numerical optimization. In: Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  7. Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider monkey optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2014)

    Article  Google Scholar 

  8. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

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Acknowledgments

The first author would like to acknowledge the Ministry of Human Resource Development, Government of India for financial support.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India

    Kavita Gupta & Kusum Deep

Authors
  1. Kavita Gupta
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  2. Kusum Deep
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Corresponding author

Correspondence to Kavita Gupta .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Dept of Mathematics, South Asian University New Delhi, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Kavita Gupta, Deep, K. (2016). Investigation of Suitable Perturbation Rate Scheme for Spider Monkey Optimization Algorithm. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_75

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_75

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

  • eBook Packages: EngineeringEngineering (R0)

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