Abstract: In view of the shortcomings of the golden jackal optimization (GJO) in solving complex optimization problems, such as slow convergence speed and being easy to fall into local optimum, a hybrid-strategy improved golden jackal optimization (IGJO) is proposed. Firstly, when the optimal solution of the algorithm stagnates updating, the Cauchy variation strategy is introduced to enhance the population diversity and improve the global search capability of the algorithm to avoid falling into local optimum. Then, a decision strategy based on weight is proposed to accelerate the convergence of the algorithm by assigning different weights to golden jackal individuals. Experiments with eight benchmark functions and some CEC2017 test functions show that the improved algorithm has better optimization performance and convergence speed. Furthermore, the improved algorithm is applied to optimize the parameters of support vector regression(SVR) model, and its effectiveness is verified by experiments on 5 UCI (University of California, Irvine) datasets.

Key words: golden jackal optimization, optimization problem, Cauchy variation, weight

摘要： 针对金豺优化算法（golden jackal optimization, GJO）在求解复杂优化问题时存在收敛速度慢和易陷入局部最优等不足，提出一种混合策略改进的金豺优化算法（improved golden jackal optimization, IGJO）。在算法的最优解停滞更新时，引入柯西变异策略，增强种群多样性和提升算法陷入局部最优的逃逸能力；提出一种基于权重的决策策略，通过对金豺个体赋予不同权重进行种群位置更新的决策，加快算法的收敛速度。对8个基准测试函数以及部分CEC2017测试函数进行寻优实验，结果表明改进算法具有更好的优化性能和收敛速度；进一步地，将改进算法应用于支持向量回归（support vector regression, SVR）模型的参数优化，并在选取的5个UCI（University of California, Irvine）数据集上进行实验，验证了改进算法的有效性。

关键词: 金豺优化算法, 优化问题, 柯西变异, 权重