基于多策略改进的金豺优化算法
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摘要：
金豺优化算法(golden jackal optimization algorithm,GJO)作为一种新型的元启发算法，由于其收敛速度精度不佳，且在探索与开采阶段平衡上存在不足，陷入局部极值等算法弊端均有出现。因此，提出了改进金豺优化算法(IGJO)。首先，采用改进型的多值 Circle混沌映射，以增进种群多样性及初始解的品质；其次，基于特定的收缩指数函数，将能量方程优化为非线性形式，实现全局与局部搜寻的有效协调；然后，引入基于t-分布的变异策略增强搜索广度，提升全局搜索效能,有效避免局部最优问题；最后，通过调整 Levy 飞行参数进行细致优化，确立了一个优化值，从而显著提升了算法的收敛速度和精确度。通过9项测试函数的实验验证表明，改进后的IGJO算法在多个方面超越了若干现有的经典或新兴算法。
Golden jackal optimization algorithm(GjO) as a new new algorithm. Due to its poor accuracy of convergence speed and deficiencies in the balance betwen exploration and mining stage, the algorithm disadvantages such as falling into local extreme value appear. Therefore, the optimization algorithm(IGJO) was proposed. Firstly, the improved multi-value Circle chaos mapping is adopted to improve the population diversity and the quality of the initial solution; secondly, based on the nonlinear form to realize the effective coordination between global and local search; thirdly, introducing t-distributed variation strategy to enhance the search breadth, improve the global search efficieney, and effectively avoid the local optimal problem: finally, by optimizing the Levy flight parameters to establish an optimization value, so as to significantly improve the convergence speed and accuracy of the algorithm. Experimental validation with nine-item test fuetions shows that the improved IGJO algorithm surpasses several existing classical or emerging algorithms in several ways.
作者：
杜晓昕，牛翔慧，王波，郝田茹，王振飞
Du Xiaoxin, Niu Xianghui, Wang Bo, Hao Tianru, Wang Zhenfei
机构地区：
齐齐哈尔大学计算机与控制工程学院；齐齐哈尔大学黑龙江省大数据网络安全检测分析重点实验室
引用本文：
杜晓昕，牛翔慧，王波等。基于多策略改进的金豺优化算法[ ] ].河南师范大学学报(自然科学版),2025,53(4):39-48.(Du Xiaoxin, Niu Xianghui, Wang Bo, et al.A modified golden jackal optization algorithm based on multiple strategies [ J ].Journal of Henan Normal University( Natural Science Edition),2025,53 (4):39-48.D01. 10.16366/j.cnki.1000-2367.2024.04.02.0001.)
基金：
黑龙江省省属高等学校基本科研业务费自然科学类青年创新人才项目
关键词：
群智能优化算法；金豺优化算法；多值 Circle 混沌映射；任意收缩指数雨数；自适应:分布突变
group inteligent optimization algorithm; golden jackal optimization algorithm; multi-value circle chaos mapping; arbitrary shrinkage index function; adaptive t distribution mutation
分类号：
TP301.6
