Article(id=1262079403283067033, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, articleNumber=null, orderNo=null, doi=10.19651/j.cnki.emt.2519162, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750521600000, receivedDateStr=2025-06-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778832778044, onlineDateStr=2026-05-15, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778832778044, onlineIssueDateStr=2026-05-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778832778044, creator=13701087609, updateTime=1778832778044, updator=13701087609, issue=Issue{id=1262079396291141802, tenantId=1146029695717560320, journalId=1260987750510510108, year='2026', volume='49', issue='6', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778832776377, creator=13701087609, updateTime=1778832914473, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1262079975688122904, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1262079975688122905, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=86, endPage=97, ext={EN=ArticleExt(id=1262079406000976041, articleId=1262079403283067033, tenantId=1146029695717560320, journalId=1260987750510510108, language=EN, title=Robot path planning research with improved artificial lemming algorithm, columnId=1262079399856300214, journalTitle=Electronic Measurement Technology, columnName=Theory and Algorithms, runingTitle=null, highlight=null, articleAbstract=

In order to solve the problems of slow convergence speed, low convergence accuracy and easy to fall into local optimization of artificial lemmings algorithm (ALA), a multi strategy improved artificial lemmings algorithm (IALA) is proposed. Firstly, Hammersley sequence is introduced to initialize the population of the algorithm, so that the initial population has better search ability; then the reverse differential mutation mechanism is used to improve the diversity of the population and enhance the ability of the algorithm to escape from the local optimum; finally, through the soft frost ice search mechanism, the algorithm takes into account the local and global characteristics in the optimization process, which improves the optimization ability and convergence speed of the algorithm. In order to verify the effectiveness of the improved algorithm, nine benchmark functions are selected to compare the improved algorithm. The comparison results show that IALA has faster convergence speed and higher convergence accuracy. Finally, the improved algorithm is applied to the simulation experiment of robot path planning on three kinds of complex maps. The results show that compared with the original algorithm ala, the improved algorithm IALA in the first kind of map, the optimal value of path decreases by 0.64%, and the average value decreases by 2.86%; in the second map, the optimal value of path decreased by 10.24%, and the average value decreased by 6.91%; in the last map, the optimal value of the path decreased by 2.6%, and the average value decreased by 1.3%. It is proved that the improved algorithm has better path optimization ability.

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为解决旅鼠算法（ALA）收敛速度慢、收敛精度低、易陷入局部最优的问题，提出一种多策略改进旅鼠算法（IALA）。首先引入Hammersley序列对算法进行种群初始化，使初始种群具有更加优秀的搜索能力；然后利用反向差分变异机制，提高种群的多样性，增强算法逃离局部最优的能力；最后通过软霜冰搜索机制，让算法在优化过程中兼顾局部性和全局性，提高了算法的寻优能力和收敛速度。为验证改进算法的有效性，选取9个基准测试函数对改进算法进行对比测试，对比结果显示：IALA有着更快的收敛速度及更高的收敛精度。最后将改进算法应用于3种复杂程度地图的机器人路径规划仿真实验，结果表明：与原算法ALA进行比较，改进后的算法IALA在第1种地图中路径最优值下降0.64%，平均值下降2.86%；在第2种地图中路径最优值下降10.24%，平均值下降6.91%；在最后一种地图中路径最优值下降2.02%，平均值下降2.6%。证明了改进后的算法具有更好的路径寻优能力。

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焦怀良，硕士研究生，主要研究方向为机器人智能控制等。E-mail: 1924980548@qq.com

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焦怀良，硕士研究生，主要研究方向为机器人智能控制等。E-mail: 1924980548@qq.com

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