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Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

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  • J. J. Jamian
  • M. N. Abdullah
  • H. Mokhlis
  • M. W. Mustafa
  • A. H. A. Bakar

Abstract

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

Suggested Citation

  • J. J. Jamian & M. N. Abdullah & H. Mokhlis & M. W. Mustafa & A. H. A. Bakar, 2014. "Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-14, February.
    • Handle: RePEc:hin:jnljam:329193
    DOI: 10.1155/2014/329193
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    Cited by:

    1. Teketay Mulu Beza & Yen-Chih Huang & Cheng-Chien Kuo, 2020. "A Hybrid Optimization Approach for Power Loss Reduction and DG Penetration Level Increment in Electrical Distribution Network," Energies, MDPI, vol. 13(22), pages 1-17, November.
    2. Fulin Fan & Giorgio Zorzi & David Campos-Gaona & Graeme Burt & Olimpo Anaya-Lara & John Nwobu & Ander Madariaga, 2021. "Sizing and Coordination Strategies of Battery Energy Storage System Co-Located with Wind Farm: The UK Perspective," Energies, MDPI, vol. 14(5), pages 1-21, March.
    3. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).

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