Using Grey Wolf Hunting Mechanism to Improve Spherical Search
Using Grey Wolf Hunting Mechanism to Improve Spherical Search
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DOI：
10.1109/iscid51228.2020.00022
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Sicheng Liu;Sichen Tao;Haichuan Yang;Lin Jiang;Yuki Todo;Shangce Gao
中科院分区：
文献类型：
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作者：
Sicheng Liu;Sichen Tao;Haichuan Yang;Lin Jiang;Yuki Todo;Shangce Gao
球形搜索算法（SS）是近年来提出的一种基于群体的元启发式算法，用于求解有界约束的非线性全局优化问题。相对于其他流行的算法，它具有相当有竞争力的性能。但它仍存在一些缺陷，如在球空间过大的情况下，不易摆脱福尔斯陷入局部最优的情况，收敛速度慢等。针对灰狼优化算法具有良好的全局搜索空间最小化和局部回避能力的特点，研究了灰狼优化算法的串行模式搜索机制，并将其与SS算法相结合，改善了灰狼优化算法在探索与开发之间的平衡性。我们提出的新的球形搜索和灰狼优化算法被称为SSGWO，并通过基于IEEE CEC2017的30个基准函数的实验结果与其组件算法进行比较，证明了其优越性。
Spherical search algorithm (SS) was a swarm-based meta-heuristic recently proposed to solve the bound-constrained non-linear global optimization problems. It has quite competitive performance with respect to other popular algorithms. Nevertheless, it still has several defects, such as it can’t easily get rid of the situation that falls into the local optimal and its convergence speed is slow under the condition that the spherical space is much too large. As grey wolf optimization (GWO) algorithm has good abilities of minimizing the global search space and local area avoidance, the search mechanism of GWO by serial pattern is studied and combined with SS to improve its balance between exploration and exploitation. The new spherical search and grey wolf optimization algorithm algorithm we proposed is called SSGWO, and its superiority is demonstrated with experimental results based on 30 benchmark functions of IEEE CEC2017 in comparison with its component algorithms.