Improving Hybrid Gravitational Search Algorithm for Adaptive Adjustment of Parameters

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI:10.1109/CIS.2017.00013

Yongchao Han, Ming Li, Jie Liu

引用次数: 1

Abstract

In this paper, a new Improved Hybrid Gravitational Search Algorithm (IHGSA) is proposed. First, the influence of the learning factor on global exploration and the local exploitation of the algorithm is analyzed, and the parameter adjustment mechanism which can balance the two search capabilities is designed reasonably. Secondly, the PSO is embedded into the Gravitational Search Algorithm (GSA), and an Improved Hybrid Gravitational Search Algorithm (IHGSA) is proposed. Finally, 21 benchmark test functions are programmed and calculated in comparison with the results of the Gravity Search Algorithm (GSA). The numerical results show that the new algorithm balanced the global exploration and local exploitation. The IHGSA are also better than the Gravity Search Algorithm (GSA) in convergence rate and convergence accuracy.

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参数自适应调整的改进混合引力搜索算法

本文提出了一种改进的混合引力搜索算法。首先，分析了学习因子对算法全局搜索和局部挖掘的影响，合理设计了能够平衡两种搜索能力的参数调整机制;其次，将粒子群算法嵌入到引力搜索算法中，提出了一种改进的混合引力搜索算法(IHGSA)。最后，编写了21个基准测试函数，并与重力搜索算法(Gravity Search Algorithm, GSA)的结果进行了对比计算。数值结果表明，新算法平衡了全局探索和局部开发。IHGSA在收敛速度和收敛精度上也优于重力搜索算法(GSA)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 13th International Conference on Computational Intelligence and Security (CIS)

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