[1]崔铭悦,莫愿斌*,王子豪,等.基于非线性自适应比例因子的雪豹优化算法[J].计算机技术与发展,2024,34(04):212-220.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 032]
　CUI Ming-yue,MO Yuan-bin*,WANG Zi-hao,et al.Snow Leopard Optimization Algorithm for Nonlinear Adaptive Scaling Factor[J].,2024,34(04):212-220.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 032]
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基于非线性自适应比例因子的雪豹优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年04期
页码:
212-220
栏目:
新型计算应用系统
出版日期:
2024-04-10

文章信息/Info

Title:
Snow Leopard Optimization Algorithm for Nonlinear Adaptive Scaling Factor
文章编号:
1673-629X(2024)04-0212-09
作者:
崔铭悦1; 2 ; 莫愿斌1; 2* ; 王子豪1; 2 ; 胡飓风1; 2
1. 广西民族大学 人工智能学院,广西 南宁 530006;
2. 广西混杂计算与集成电路设计分析重点实验室,广西 南宁 530006
Author(s):
CUI Ming-yue1; 2 ; MO Yuan-bin1; 2* ; WANG Zi-hao1; 2 ; HU Ju-feng1; 2
1. School of Artificial Intelligence,Guangxi Minzu University,Nanning 530006,China;
2. Guangxi Key Laboratory of Hybrid Computation & IC Design Analysis,Nanning 530006,China
关键词:
雪豹优化算法; 混沌映射; 非线性自适应比例因子; 差分进化算子; 约束优化问题
Keywords:
snow leopard optimization algorithm;  chaotic mapping;  nonlinear adaptive scaling factor;  differential evolution operator; constraint optimization problems
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 04. 032
摘要:
针对雪豹优化算法在求解复杂优化问题时,存在全局勘探能力不足、寻优精度低等问题,提出一种改进的雪豹优化算法。 首先,基于分段 Logistic 混沌映射初始化从而提高初始种群多样性;其次,引入非线性比例因子用于平衡算法的全局勘探能力和局部开发能力;然后,提出了一种差分变异策略,在第一次种群更新位置后,使用 5 个随机个体提高全局搜索能力和算法
收敛能力,在第二次种群更新位置后,使用 3 个随机个体保证在求解过程的中后期也具有一定的全局勘探能力,尽可能避免陷入局部最优。 通过在 IEEE CEC2022 基准函数测试集上
测试,并与其他算法进行比较,结果表明所提出的算法在种群质量、求解精度以及算法稳定性上均有较大提升。 最后将所提出的算法应用于工程优化,计算结果进一步证实了算法的强优化能力。
Abstract:
An improved snow leopard optimization algorithm is proposed to solve complex optimization problems, such as insufficientglobal exploration ability and low optimization accuracy. Firstly,the initial population diversity is improved based on piecewise Logisticchaotic mapping initialization. Secondly,a nonlinear scaling factor is introduced to balance the global exploration ability and local development ability of the algorithm. Then,a differential variation strategy is proposed. After the first population update position,five randomindividuals are used to improve the global search ability and the convergence ability of the algorithm. After the second population updateposition,three random individuals are used to ensure the global exploration ability in the middle and late period of the solution process, soas to avoid falling into the local optimal as much as possible. The proposed algorithm is tested on IEEE CEC2022 benchmark functiontest set and compared with other algorithms. The results show that the proposed algorithm has great improvement in population quality,　solving accuracy and algorithm stability. Finally,the proposed algorithm is applied to engineering optimization,and the calculation resultsfurther confirm its strong optimization ability.

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更新日期/Last Update: 2024-04-10