Gravitational Search Algorithm With Hierarchical Structure Guided By Elite Individual
Gravitational Search Algorithm With Hierarchical Structure Guided By Elite Individual
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DOI：
10.1109/iscid56505.2022.00047
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Haotian Li;Yifei Yang;Haichuan Yang;Zheng Tang;Shangce Gao
中科院分区：
文献类型：
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作者：
Haotian Li;Yifei Yang;Haichuan Yang;Zheng Tang;Shangce Gao
引力搜索算法（GSA）是一种基于牛顿引力的元启发式算法。该算法根据种群成员之间的引力作用来更新种群成员的位置，以解决优化问题。然而，在某些情况下，算法的收敛速度不够快。为了提高算法的收敛速度，同时缓解算法容易陷入局部最优的问题，提出了一种精英个体引导的层次结构引力搜索算法（EHGSA）。这种结构可以帮助算法在提高收敛速度的同时具有跳出局部最优的能力。使用IEEE CEC2017基准测试集的29个函数验证了所提出的算法EHGSA的有效性。
The Gravitational Search Algorithm (GSA) is a Newtonian gravity-based meta-heuristic algorithm. The algorithm updates population members’ locations according to the gravitational pull between them in order to solve the optimization issue. However, in some cases, the convergence of the algorithm is not fast enough. In order to improve the convergence speed of the algorithm while alleviating the problem of easily falling into local optima, we propose a gravitational search algorithm with hierarchical structure (EHGSA) guided by elite individuals. This structure can help the algorithm to have the ability to jump out of the local optimum while improving the convergence speed. The effectiveness of the proposed algorithm EHGSA is validated using the IEEE CEC2017 benchmark function test set of 29 functions.