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A New Collaborative Approach to Particle Swarm Optimization for Global Optimization

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

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

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Abstract

Particle swarm optimization (PSO) is population-based metaheuristic algorithm which mimics animal flocking behavior for food searching and widely applied in various fields. In standard PSO, movement behavior of particles is forced by the current bests, global best and personal best. Despite moving toward the current bests enhances convergence, however, there is a high chance for trapping in local optima. To overcome this local trapping, a new updating equation proposed for particles so-called extraordinary particle swarm optimization (EPSO). The particles in EPSO move toward their own targets selected at each iteration. The targets can be the global best, local bests, or even the worst particle. This approach can make particles jump from local optima. The performance of EPSO has been carried out for unconstrained benchmarks and compared to various optimizers in the literature. The optimization results obtained by the EPSO surpass those of standard PSO and its variants for most of benchmark problems.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (NRF-2013R1A2A1A01013886).

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Authors and Affiliations

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

    Joong Hoon Kim, Thi Thuy Ngo &  Ali Sadollah

Authors
  1. Joong Hoon Kim
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  2. Thi Thuy Ngo
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  3. Ali Sadollah
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Corresponding author

Correspondence to Joong Hoon Kim .

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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Kim, J.H., Ngo, T.T., Ali Sadollah (2016). A New Collaborative Approach to Particle Swarm Optimization for Global Optimization. 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_57

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_57

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

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