• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2023, Vol. 45 ›› Issue (07): 1320-1330.

• 人工智能与数据挖掘 • 上一篇    

融合正余弦策略的算术优化算法

黄学雨1,2,罗华3   

  1. (1.江西理工大学软件工程学院,江西 南昌 330013;2.南昌市虚拟数字工程与文化传播重点实验室,江西 南昌 330013;
    3.江西理工大学信息工程学院,江西 赣州 341000)

  • 收稿日期:2022-02-25 修回日期:2022-04-19 接受日期:2023-07-25 出版日期:2023-07-25 发布日期:2023-07-11
  • 基金资助:
    国家重点研发计划重点专项(2020YFB1713700)

An arithmetic optimization algorithm integrating sine-cosine strategy

HUANG Xue-yu1,2,LUO Hua3   

  1. (1.School of Software Engineering,Jiangxi University of Science and Technology,Nanchang 330013;
    2.Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications,Nanchang 330013;
    3.School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)
  • Received:2022-02-25 Revised:2022-04-19 Accepted:2023-07-25 Online:2023-07-25 Published:2023-07-11

摘要: 针对算术优化算法存在的求解精度低、收敛速度慢和容易陷入局部最优等缺陷,提出一种融合正弦余弦策略的算术优化算法。根据个体适应度的变化信息自适应调整数学优化器加速函数MOA,平衡算法的全局探索和局部开发能力;将改进后的正弦余弦算法引入算法的局部开发阶段,增加迭代后期种群多样性,避免算法陷入局部最优,有效提升算法的求解精度和收敛速度。在14个基准测试函数上的仿真实验结果表明,改进算法在求解精度、收敛速度和鲁棒性方面均有明显提升。最后将改进算法用于支持向量机SVM参数优化,并建立学生知识水平预测模型,进一步验证了算法的实用性和优越性。

关键词: 算术优化算法, 自适应, 正弦余弦算法, 函数优化

Abstract: This paper proposes an arithmetic optimization algorithm that integrates the sine-cosine strategy to address the problems of low solution accuracy, slow convergence speed, and easy fall into local optima in arithmetic optimization algorithms. The algorithm adaptively adjusts the math optimizer acceleration (MOA) accelerator function based on the change information of individual fitness, balancing the global exploration and local exploitation abilities of the algorithm. The improved sine-cosine algorithm is introduced into the local development stage of the algorithm, increasing the population diversity in the later iterations, avoiding the algorithm from falling into local optima, and effectively improving the solution accuracy and convergence speed of the algorithm. Simulation experiments on 14 benchmark test functions show that the improved algorithm has significant improvements in solution accuracy, convergence speed, and robustness. Finally, the improved algorithm is applied to the optimization of support vector machine (SVM) parameters, and a student knowledge level prediction model is established, which further verifies the practicality and superiority of the algorithm.

Key words: arithmetic optimization algorithm, adaptive, sine-cosine algorithm, function optimization