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A Niching Co-swarm Gravitational Search Algorithm for Multi-modal Optimization

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Proceedings of Fourth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 335))

Abstract

In this paper, a Niching co-swarm gravitational search algorithm (CoGSA) is designed for solving multi-modal optimization problems. The collective approach of Gravitational Search Algorithm and differential evolution (DE) is used to solve multi-modal optimization problems. A set of twelve multi-modal problems are taken from a benchmark set of CEC 2013. An experimental study has been performed to evaluate the availability of CoGSA over these twelve problems. The performance is measured in an advanced way. It has been observed that CoGSA provides good solution for multi-modal optimization problems.

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References

  1. Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: Evolutionary Computation, 2004. CEC2004. Congress on, IEEE, vol. 2, pp. 1382–1389 (2004)

    Google Scholar 

  2. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Evol. Comput. IEEE Trans. 10(3), 281–295 (2006)

    Article  Google Scholar 

  3. Yadav, A., Deep, K.: Shrinking hypersphere based trajectory of particles in PSO. Appl. Math. Comput. 220, 246–267 (2013)

    Article  Google Scholar 

  4. Deb, K., Saha, A.: Multimodal optimization using a bi-objective evolutionary algorithm. Evol. Comput. 20(1), 27–62 (2012)

    Article  Google Scholar 

  5. Qu, B.-Y., Suganthan, P.N., Das, S.: A distance-based locally informed particle swarm model for multimodal optimization. IEEE Trans. Evol. Comput. 17(3), 387–402 (2013)

    Article  Google Scholar 

  6. Qu, B.-Y., Liang, J.J., Suganthan, P.N.: Niching particle swarm optimization with local search for multi-modal optimization. Inf. Sci. 197, 131–143 (2012)

    Article  Google Scholar 

  7. Yadav, A., Deep, K.: A novel co-swarm gravitational search algorithm for constrained optimization. In: Proceedings of the Third International Conference on Soft Computing for Problem Solving, pp. 629–640. Springer, Berlin (2014)

    Google Scholar 

  8. Li, X., Engelbrecht, A., Epitropakis, M.G.: Benchmark functions for CEC’2013 special session and competition on niching methods for multimodal function optimization. Evolutionary Computation and Machine Learning Group, RMIT University, Melbourne Tech. Rep. (2013)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  10. 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  MATH  MathSciNet  Google Scholar 

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Acknowledgment

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2013R1A2A1A01013886).

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Authors and Affiliations

  1. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 136-713, South Korea

    Anupam Yadav & Joong Hoon Kim

Authors
  1. Anupam Yadav
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  2. Joong Hoon Kim
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Corresponding author

Correspondence to Anupam Yadav .

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Editors and Affiliations

  1. Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  3. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

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Yadav, A., Kim, J.H. (2015). A Niching Co-swarm Gravitational Search Algorithm for Multi-modal Optimization. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_48

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  • DOI: https://doi.org/10.1007/978-81-322-2217-0_48

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2216-3

  • Online ISBN: 978-81-322-2217-0

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