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International Conference on Intelligent Systems Design and Applications

ISDA 2018 2018: Intelligent Systems Design and Applications pp 821–831Cite as

Opposition Based Salp Swarm Algorithm for Numerical Optimization

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

Abstract

In this paper an improved optimization algorithm called Opposition Based Salp Swarm Algorithm (OSSA) is proposed. This is improved version of recently proposed Salp Swarm Algorithm (SSA), which mimics swarming acts of salps when foraging and navigating in oceans. To improve the performance of SSA, Opposition based learning (OBL) is introduced in Salp Swarm Algorithm. The algorithm is evaluated on several numerical standard functions and is compared with some well known optimization algorithms.

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References

  1. Glover, F.W., Kochenberger, G.A. (eds.): Handbook of Metaheuristics, vol. 57. Springer, Berlin (2006)

    MATH  Google Scholar 

  2. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science 1995. MHS’95, pp. 39–43. IEEE (1995)

    Google Scholar 

  3. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  4. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)

    Article  Google Scholar 

  5. Karaboga, D.: An idea based on honey bee swarm for numerical optimization, vol. 200. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  6. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017)

    Article  Google Scholar 

  7. Połap, D.: Polar bear optimization algorithm: meta-heuristic with fast population movement and dynamic birth and death mechanism. Symmetry 9(10), 203 (2017)

    Article  Google Scholar 

  8. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  10. Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  11. Rechenberg, I.: Evolution strategy: nature’s way of optimization. In: Optimization: Methods and Applications, Possibilities and Limitations, pp. 106–126. Springer, Berlin (1989)

    Google Scholar 

  12. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  14. Kaveh, A., Talatahari, S.: A novel heuristic optimization method: charged system search. Acta Mech. 213, 267–289 (2010)

    Article  Google Scholar 

  15. Mirjalili, S., Mirjalili, S.M., Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2016)

    Article  Google Scholar 

  16. Glover, F.: Tabu search - Part I. ORSA J. Comput. 1(3), 190–206 (1989)

    Article  MathSciNet  Google Scholar 

  17. Glover, F.: Tabu search - Part II. ORSA J. Comput. 2, 4–32 (1990)

    Article  Google Scholar 

  18. He, S., Wu, Q., Saunders, J.: A novel group search optimizer inspired by animal behavioural ecology. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC, pp. 1272–1278 (2006)

    Google Scholar 

  19. He, S., Wu, Q.H., Saunders, J.: Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans. Evol. Comput. 13, 973–990 (2009)

    Article  Google Scholar 

  20. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Advances in Swarm Intelligence, pp. 355–364. Springer, Heidelberg (2010)

    Google Scholar 

  21. Hertz, J.: Introduction to the Theory of Neural Computation, vol. 1. Addison Wesley, Boston (1991)

    Google Scholar 

  22. Rumelhart, D.E., Williams, R.J., Hinton, G.E.: Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362 (1986)

    Google Scholar 

  23. Mendes, R., Cortez, P., Rocha, M., Neves, J.: Particle swarm for feedforward neural network training. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1895–1899 (2002)

    Google Scholar 

  24. Meissner, M., Schmuker, M., Schneider, G.: Optimized particle swarm optimization (OPSO) and its application to artificial neural network training. BMC Bioinform. 7, 125 (2006)

    Article  Google Scholar 

  25. Fan, H., Lampinen, J.: A trigonometric mutation operation to differential evolution. J. Glob. Optim. 27, 105–129 (2003)

    Article  MathSciNet  Google Scholar 

  26. Slowik, A., Bialko, M.: Training of artificial neural networks using differential evolution algorithm. In: Human System Interactions, pp. 60–65 (2008)

    Google Scholar 

  27. Gao, Q., Qi, K., Lei, Y., He, Z.: An improved genetic algorithm and its application in artificial neural network. In: 2005 Fifth International Conference on Information, Communications and Signal Processing, 06–09 December 2005, pp. 357–360 (2005)

    Google Scholar 

  28. Olorunda, O., Engelbrecht, A.P.: Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE Congress on Evolutionary Computation. CEC 2008 (IEEE World Congress on Computational Intelligence), pp. 1128–1134. IEEE (2008)

    Google Scholar 

  29. Alba, E., Dorronsoro, B.: The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans. Evol. Comput. 9(2), 126–142 (2005)

    Article  Google Scholar 

  30. Crepinsek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)

    Article  Google Scholar 

  31. Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, vol. 1, pp. 695–701. IEEE, November 2005

    Google Scholar 

  32. Ali, M.M., Khompatraporn, C., Zabinsky, Z.B.: A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J. Glob. Optim. 31(4), 635–672 (2005)

    Article  MathSciNet  Google Scholar 

  33. Bansal, J.C., Sharma, H., Nagar, A., Arya, K.V.: Balanced artificial bee colony algorithm. Int. J. Artif. Intell. Soft Comput. 3(3), 222–243 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Malaviya National Institute of Technology Jaipur, Jaipur, India

    Divya Bairathi & Dinesh Gopalani

Authors
  1. Divya Bairathi
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  2. Dinesh Gopalani
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Corresponding author

Correspondence to Divya Bairathi .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Bairathi, D., Gopalani, D. (2020). Opposition Based Salp Swarm Algorithm for Numerical Optimization. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_80

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