摘要
针对天鹰优化算法搜索效率不足,容易陷入局部最优的缺点,提出多策略改进天鹰优化算法(MIAO).引入广义正态分布优化算法(GNDO),将该算法得出的结果与天鹰优化算法第1阶段得出的结果进行比较,筛选出这2种优化算法下的最优值.该操作扩大了搜索空间,提高了解的质量.引入相量算子,将第2阶段变为自适应的非参数优化,提高算法的高维优化能力.针对天鹰优化算法在迭代后期存在种群多样性降低、局部开发能力不足的问题,在天鹰算法的第3阶段引入流向算子,使信息可以在每个个体间相互传递,提高种群信息的利用率,增强天鹰优化算法的开发性能.通过对16个测试函数寻优对比分析以及Wilcoxon秩和检验可知,MIAO的寻优能力和收敛速度都有较大的提升.为了验证MIAO算法的实用性和可行性,采用所提算法求解减速器设计问题,通过实际工程优化问题的实验对比分析可知,MIAO算法在处理现实优化问题上具有一定的优越性.
Abstract
A multi-strategy improved aquila optimizer(MIAO)was proposed aiming at the disadvantages of low search efficiency and easy to fall into local optimal value in aquila optimization algorithm.Generalized normal distribution optimizer(GNDO)was added to AO,and the result obtained by GNDO was compared with the result of AO in the first stage.Then the best value under the two algorithms was selected.The search space was expanded and the quality of solution was improved.Phasor operators were used to transform the second phase into an adaptive non-parametric optimization in order to improve the high-dimensional optimization ability of the AO.The flow operator was used in the third stage of AO aiming at the problems of reduced population diversity and insufficient local exploitation at late iterations of the AO.Then the information can be transferred between each individual.The utilization rate of population information was improved,and the local exploitation capability of the AO was enhanced.Comparative analysis and optimization results of 16 test functions and Wilcoxon rank sum test showed that MIAO optimization ability and convergence speed were greatly improved.The MIAO algorithm was used to solve reducer design problem in order to verify the practicality and feasibility of MIAO algorithm.Comparative analysis of practical engineering optimization problems shows that MIAO algorithm has certain advantages in processing realistic optimization problems.
基金项目
国家自然科学基金(U1504622)
国家自然科学基金(31671580)
河南省高等学校青年骨干教师培养计划(2018GGJS079)
河北省高等学校科学技术研究项目(ZD2020344)
维普
万方数据