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

Hongping Hu, Xiaxia Cui and Yanping Bai

Advances in Mathematical Physics, 2017, vol. 2017, issue 1

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.

Date: 2017
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https://doi.org/10.1155/2017/2131862

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnlamp:v:2017:y:2017:i:1:n:2131862

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