灰狼与郊狼混合优化算法及其聚类优化

郊狼优化算法(Coyote optimization algorithm, COA)是最近提出的一种新颖且具有较大应用潜力的群智能优化算法, 具有独特的搜索机制和能较好解决全局优化问题等优势, 但在处理复杂优化问题时存在搜索效率低、可操作性差和收敛速度慢等不足. 为弥补其不足, 并借鉴灰狼优化算法(Grey wolf optimizer, GWO)的优势, 提出了一种COA与GWO的混合算法(Hybrid COA with GWO, HCOAG). 首先提出了一种改进的COA (Improved COA, ICOA), 即将一种高斯全局趋优成长算子替换原算法的成长算子以提高搜索效率和收敛速度, 并提出一种动态调整组内郊狼数方案, 使得算法的搜索能力和可操作性都得到增强; 然后提出了一种简化操作的GWO (Simplified GWO, SGWO), 以提高算法的可操作性和降低其计算复杂度; 最后采用正弦交叉策略将ICOA与SGWO二者融合, 进一步获得更好的优化性能. 大量的经典函数和CEC2017复杂函数优化以及K-Means聚类优化的实验结果表明, 与COA相比, HCOAG具有更高的搜索效率、更强的可操作性和更快的收敛速度, 与其他先进的对比算法相比, HCOAG具有更好的优化性能, 能更好地解决聚类优化问题.

Hybrid Coyote Optimization Algorithm With Grey Wolf Optimizer and Its Application to Clustering Optimization

Coyote optimization algorithm (COA) is a novel swarm intelligence optimization algorithm with great application potential, which was proposed recently. It has a unique search mechanism and the advantages to solve global optimization problems well and so on. But when dealing with the complex optimization problems, it has some defects, such as low search efficiency, poor operability, slow convergence speed and so on. To make up for COA's disadvantages and utilize the advantages of grey wolf optimizer (GWO), a hybrid COA with GWO (HCOAG) is proposed. Firstly, an improved COA (ICOA) is proposed. A Gaussian global-best growing operator replaces the growing operator of the original algorithm to improve the search efficiency and convergence speed, and a dynamic adjustment scheme of coyote number in each group is proposed to enhance the search ability and operability. Secondly, in order to improve the operability and reduce the computational complexity of the algorithm, a simplified GWO (SGWO) is proposed. Finally, ICOA and SGWO are integrated by a sinusoidal crossover strategy to further get better optimization performance. A large number of experimental results on classical benchmark functions and CEC2017 complex functions and K-Means clustering show that, compared with COA, HCOAG has higher search efficiency, stronger operability and faster convergence speed. Compared with other state-of-the-art comparison algorithms, HCOAG has better optimization performance and can solve clustering optimization problems better.