为求解高维优化问题,提出基于反向学习和衰减因子的灰狼优化算法(grey wolf algorithm based on opposition learning and reduction factor,ORGWO).设计一种灰狼反向学习模型,模型考虑问题搜索边界信息和种群历史搜索信息,初始种群阶段增加反向学习,增强种群多样性.根据算法各个阶段不同特征引入衰减因子,平衡全局和局部勘探能力.选取8个高维函数和23个不同特征的优化函数对算法性能进行测试,进一步使用收敛性分析,寻优成功率,CPU时间,Wilcoxon秩和检验来评估改进算法,实验结果表明,ORGWO算法在求解高维问题上具有较好的精度,鲁棒性和更快的收敛速度.
Improved grey wolf algorithm to solve high-dimensional optimization problems
This paper proposes a grey wolf algorithm based on opposition-based learning and an attenuation factor to solve high dimensional optimization problems(ORGWO).An opposition-based learning model is designed,considering the boundary search information and the population historical search information.In the initial population stage,an opposition-based learning is added,which enhances the diversity of the pop-ulation.An attenuation factor is introduced to balance the global and local exploration capabilities.Eight high-dimensional test functions and twenty-three benchmark functions with different characteristics are used to test the performance of the algorithm.The convergence analysis,success rate,CPU time,and Wilcoxon rank sum test are used to evaluate the ORGWO.The experimental results show that the ORGWO has better precision,robustness and faster convergence speed in solving high dimensional problems.
grey wolf optimization algorithmopposition learningreduction factorhigh dimensional opti-mization problem
万方数据
维普
