2024 06 v.56 25-31
一种融合反向学习机制与差分进化策略的蛇优化算法
基金项目(Foundation):
国家自然科学基金项目(61966002);;
江西省学位与研究生教育教学改革研究项目(JXYJG-2022-172)
邮箱(Email):
wthpku@163.com;
DOI:
10.13705/j.issn.1671-6841.2023113
中文作者单位:
赣南师范大学数学与计算机科学学院;
摘要(Abstract):
蛇优化(snake optimizer, SO)算法存在前期收敛速度慢和易陷入局部最优的问题,为此提出一种融合反向学习机制与差分进化策略的改进蛇优化(improved snake optimizer, ISO)算法。反向学习机制可提高种群质量,以提升算法寻优速度;差分进化策略有助于算法精准寻优,降低算法陷入局部最优的几率。在10个基准测试函数上的实验结果表明,ISO算法拥有更高的寻优精度和更快的收敛速率。将其应用于支持向量机(support vector machine, SVM)的参数选取中,进一步验证了ISO算法的有效性。
关键词(KeyWords):
蛇优化算法;;差分进化;;反向学习;;参数优化;;支持向量机
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[5] OPARA K R,ARABAS J.Differential Evolution:a survey of theoretical analyses[J].Swarm and evolutionary computation,2019,44:546-558.
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[7] 刘拥民,王靖枫,黄浩,等.模拟人工蜂群的高维数据特征选择算法研究[J].郑州大学学报(理学版),2023,55(3):57-64.LIU Y M,WANG J F,HUANG H,et al.Research on high-dimensional data feature selection algorithm simulating artificial bee colony[J].Journal of Zhengzhou university (natural science edition),2023,55(3):57-64.
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[10] CAI B,ZHU X P,QIN Y X.Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm[J].Expert systems with applications,2021,168:114388.
[11] WANG E L,XIA J Y,LI J,et al.Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm[J].Energy,2022,261:125146.
[12] LI S,XU K,XUE G Z,et al.Prediction of coal spontaneous combustion temperature based on improved grey wolf optimizer algorithm and support vector regression[J].Fuel,2022,324:124670.
[13] 李天翼,陈红梅.一种用于解决特征选择问题的新型混合演化算法[J].郑州大学学报(理学版),2021,53(2):41-49.LI T Y,CHEN H M.A new hybrid evolutionary algorithm for solving feature selection problem[J].Journal of Zhengzhou university (natural science edition),2021,53(2):41-49.
[14] CHAKRABORTY S,KUMAR SAHA A,SHARMA S,et al.A novel enhanced whale optimization algorithm for global optimization[J].Computers & industrial engineering,2021,153:107086.
[15] ELAZIZ M A,MIRJALILI S.A hyper-heuristic for improving the initial population of whale optimization algorithm[J].Knowledge-based systems,2019,172:42-63.
[16] DEHGHANI M,HUBALOVSKY S,TROJOVSKY P.Northern goshawk optimization:a new swarm-based algorithm for solving optimization problems[J].IEEE access,2021,9:162059-162080.
[17] HU G,YANG R,ABBAS M,et al.BEESO:multi-strategy boosted snake-inspired optimizer for engineering applications[J].Journal of bionic engineering,2023,20(4):1791-1827.
[18] ABU KHURMA R,ALBASHISH D,BRAIK M,et al.An augmented Snake Optimizer for diseases and COVID-19 diagnosis[J].Biomedical signal processing and control,2023,84:104718.
[19] TIZHOOSH H R.Opposition-based learning:a new scheme for machine intelligence[C]//International Conference on Computational Intelligence for Modelling,Control and Automation and International Conference on Intelligent Agents,Web Technologies and Internet Commerce.Piscataway:IEEE Press,2006:695-701.
基本信息:
DOI:10.13705/j.issn.1671-6841.2023113
中图分类号:TP18
引用信息:
[1]占宏祥,汪廷华,张昕.一种融合反向学习机制与差分进化策略的蛇优化算法[J].郑州大学学报(理学版),2024,56(06):25-31.DOI:10.13705/j.issn.1671-6841.2023113.
基金信息:
国家自然科学基金项目(61966002);; 江西省学位与研究生教育教学改革研究项目(JXYJG-2022-172)
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