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中文摘要：
提出一种基于混沌和精英反向学习的混合灰狼优化算法以解决高维优化问题.首先,采用混沌序列产生初始种群为算法进行全局搜索奠定基础;然后,对当前精英个体分别执行精英反向学习策略以协调算法的勘探和开采能力;最后,在搜索过程中对决策层个体进行混沌扰动,以避免算法陷入局部最优的可能性.选取10个高维（100维、500维和1 000维）标准测试函数进行数值实验,结果表明,混合灰狼优化算法在求解精度及收敛速度指标上均明显优于对比算法.
英文摘要：
A hybrid grey wolf optimization（HGWO） algorithm combined chaotic mapping and elite opposition-based learning strategy is proposed for solving unconstrained high-dimensional function optimization problems. In the proposed HGWO algorithm, the chaotic sequence is used to initiate the individuals＇ position, which can strengthen the diversity of global searching. Then the elite opposition-based learning strategy is applied to the current elite individuals, which can coordinate the exploration and exploitation ability of the proposed HGWO algorithm. Finally, the first three best individuals are disturbed by chaotic mapping in the process of the search so as to avoid the possibility of falling into local optimum. Numerical experiments are conducted on the 10 high-dimensional（100, 500, and 1000 dimension） classical test functions. The simulation results show that the proposed HGWO algorithm has better performance in solution precision and convergence speed.
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