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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。
Abstract：To address the problem of slow convergence speed in the early stage and local optimization, an improved snake optimizer(ISO) algorithm based on reverse learning mechanism and differential evolution strategy was proposed. The reverse learning mechanism could improve the population quality to enhance optimization speed.The differential evolution strategy could help search accurately and reduce the probability of falling into local optimal value.The experimental results of ten benchmark functions showed that ISO had higher optimization accuracy and faster convergence rate. It was later applied to the parameter selection of the support vector machine(SVM) to demonstrate the effectiveness of the proposed ISO algorithm.
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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)