摘要
为了解决麻雀搜索算法(SSA)存在的跳出局部最优值能力弱、寻优精度不理想等缺陷,提出了一种基于自适应权重因子与透镜成像反向学习的麻雀搜索算法(LIW-SSA)。利用Circle映射和一般反向学习策略生成麻雀种群,提升了初始种群的质量以及丰富性。将自适应权重因子引入到麻雀种群警戒者更新公式中,能有效平衡算法前后期搜索能力。采用透镜成像学习机制对当前麻雀最优个体实施干扰,提高了算法的跳出局部最优以及寻优性能。通过对基准测试函数的寻优对比,验证了提出的LIW-SSA算法相较于基本麻雀搜索算法以及其他优化算法,在算法稳定性以及寻优精度上得到了很大的提高。
In order to solve the shortcomings of Sparrow Search Algorithm(SSA),such as weak ability to jump out of local optimal value and unsatisfactory optimization accuracy,a Sparrow Search Algorithm based on Lens Imaging reverse learning and adaptive Weight factor(LIW-SSA)is proposed.The sparrow population is generated by using Circle mapping and general reverse learning strategy,which improves the quality and richness of the initial population.The adaptive weight factor is introduced into the sparrow population alarm update formula to effectively balance the pre and post period search ability of the algorithm.The lens imaging learning mechanism is used to interfere with the current sparrow optimal individual,so as to improve the jumping out of local optimization and optimization performance of the algorithm.Through the optimization comparison of benchmark functions,it is verified that the proposed LIW-SSA algorithm has greatly improved the stability and optimization accuracy of the algorithm compared with the basic sparrow search algorithm and other optimization algorithms.
作者
史洪岩
蔡志豪
SHI Hongyan;CAI Zhihao(Department of Information Engineering,Shenyang University of Chemical and Technology,Shenyang 110142,China)
出处
《电子设计工程》
2024年第5期13-18,24,共7页
Electronic Design Engineering
基金
国家重点研发计划(2018YFB1700200)
辽宁省自然科学基金项目(2019-ZD-0069)。
