摘要
针对麻雀搜索算法(sparrow search algorithm,SSA)求解精度依赖于较优位置的群体,易于陷入局部最优等问题,提出改进型的麻雀搜索算法(improved sparrow search algorithm,ISSA).该算法首先提出正态偏移策略,以重心位置为导向进行种群偏移,实现移动能量的正态分布衰减,有效提升种群对局部搜索的勘探能力;其次引入动态正弦扰动策略,通过缩放因子实现发现者对前期搜索步长和后期快速收敛的双向需求.然后针对麻雀种群中位置较差的预警者加入反向学习机制,以预警者当前位置生成扰动的反向解,有利于扩大搜索步长,帮助算法跳出局部最优.最后随机选取6个测试函数并与其他算法进行比较,实验结果验证了ISSA算法的有效性.
To address the problem that the solution accuracy of the sparrow search algorithm(SSA)depends on the population at the better location and is easily trapped in the local optimum,this study proposes an improved sparrow search algorithm(ISSA).The algorithm firstly proposes a normal shift strategy to shift the population with the center of gravity as the guide to achieve the decay of the normal distribution of the moving energy and effectively improve the exploration ability of the population for local search.Secondly,it introduces a dynamic sinusoidal perturbation strategy to achieve the two-way demands of the discoverer for the early search step and the late fast convergence through the scaling factor.Then,a backward learning mechanism is added for the poorly positioned early warners in the sparrow population to generate the backward solution of the perturbation with their current position,which is helpful to expand the search step and enable the algorithm to jump out of the local optimum.Finally,six test functions are randomly selected and compared with other similar algorithms,and the experimental results verify the effectiveness of the ISSA algorithm.
作者
李书杭
童楠
符强
LI Shu-Hang;TONG Nan;FU Qiang(School of Information Engineering,College of Science&Technology Ningbo University,Ningbo 315300,China)
出处
《计算机系统应用》
2023年第12期205-210,共6页
Computer Systems & Applications
基金
宁波市自然科学基金(2021J135)。
关键词
麻雀搜索算法
正态偏移
动态正弦扰动
反向学习
sparrow search algorithm(SSA)
normal offset
dynamic sinusoidal perturbation
reverse learning
