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Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm

Author

Listed:
  • Hongping Hu
  • Xiaxia Cui
  • Yanping Bai

Abstract

Gravitational Search Algorithm (GSA) is a widely used metaheuristic algorithm. Although fewer parameters in GSA were adjusted, GSA has a slow convergence rate. In this paper, we change the constant acceleration coefficients to be the exponential function on the basis of combination of GSA and PSO (PSO‐GSA) and propose an improved PSO‐GSA algorithm (written as I‐PSO‐GSA) for solving two kinds of classifications: surface water quality and the moving direction of robots. I‐PSO‐GSA is employed to optimize weights and biases of backpropagation (BP) neural network. The experimental results show that, being compared with combination of PSO and GSA (PSO‐GSA), single PSO, and single GSA for optimizing the parameters of BP neural network, I‐PSO‐GSA outperforms PSO‐GSA, PSO, and GSA and has better classification accuracy for these two actual problems.

Suggested Citation

  • Hongping Hu & Xiaxia Cui & Yanping Bai, 2017. "Two Kinds of Classifications Based on Improved Gravitational Search Algorithm and Particle Swarm Optimization Algorithm," Advances in Mathematical Physics, John Wiley & Sons, vol. 2017(1).
    • Handle: RePEc:wly:jnlamp:v:2017:y:2017:i:1:n:2131862
    DOI: 10.1155/2017/2131862
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    References listed on IDEAS

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