首页|求解函数优化和特征选择的改进金豺狼优化算法

求解函数优化和特征选择的改进金豺狼优化算法

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针对基本金豺狼优化算法(Golden Jackal Optimization,GJO)在解决高维优化问题时存在计算精度低、开发能力弱、容易陷入局部最优的缺点,提出一种改进GJO算法(I-GJO).在改进算法中,设计一种基于正弦函数的非线性能量因子替代原随机递减能量因子,以平衡算法在搜索过程中的全局探索和局部开发能力.在算法迭代后期引入翻筋斗学习策略,从而扩大群体搜索范围和改善解的精度.为了验证I-GJO算法的有效性,选取6个基准函数优化问题进行数值实验,并与灰狼优化、海鸥优化算法和基本GJO算法比较.结果表明,I-GJO获得较高的精度和较快的收敛速度.最后利用I-GJO算法求解特征选择问题,对16个基准数据集的数值结果显示,改进算法能有效去除冗余特征和提高分类精度.
Improved Golden Jackal Optimization Algorithm for Solving Function Optimization and Feature Selection
The basic Golden Jackal Optimization algorithm(GJO)had several drawbacks such as low computation precision,poor exploitation,and ease to get stuck in a local optima when solving high-dimensional optimization problems.An improved GJO algorithm(I-GJO)was proposed.In I-GJO,the original randomly decreasing energy factor was replaced by a nonlinear decreasing factor based on sine function to balance the global exploration and local exploitation abilities of algorithm during the search process.In the later iterative stage of algorithm,a somersault learning strategy was introduced to expand the population search region and improved the solution precision.In order to verify the effectiveness of the proposed I-GJO algorithm,six benchmark function optimization problems were selected for experiment.The experimental results indicated that I-GJO had higher precision and faster convergence speed than the Grey Wolf optimizer(GWO),Seagull Optimization Algorithm(SOA)and the basic GJO algorithm.Finally,I-GJO was applied to solve the feature selection problem.The numerical results on sixteen benchmark datasets showed that I-GJO could effectively remove the redundant features and improve the classification accuracy.

Golden Jackal Optimization algorithmsomerault learning strategyfunction optimizationfeature selection

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上海工程技术大学电子电气工程学院,上海 201620

贵州财经大学数学与统计学院,贵州贵阳 550025

金豺狼优化算法 翻筋斗学习策略 函数优化 特征选择

国家自然科学基金项目贵州省自然科学基金重点项目贵州省高层次创新型人才项目贵州省高等学校系统建模与数据挖掘重点实验室项目

12361106黔科合基础-ZK[2023]重点003黔科合平台人才-GCC[2023]0062023013

2024

10.3969/j.issn.1003-0972.2024.01.017

信阳师范学院学报(自然科学版)
信阳师范学院

信阳师范学院学报(自然科学版)

CSTPCD
影响因子：0.446
ISSN：1003-0972
年,卷(期)：2024.37(1)
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