| 9 | 0 | 40 |
| 下载次数 | 被引频次 | 阅读次数 |
目的:为应对传统进化算法在处理高维昂贵优化问题时面临的维度灾难和计算成本高昂等挑战，提出一种基于改进山地瞪羚优化器的增量克里金辅助进化算法(incremental Kriging-assisted evolutionary algorithm based on improved mountain gazelle optimizer, IKAEA-IMGO)。方法:针对山地瞪羚优化器(mountain gazelle optimizer, MGO)中生成个体出现无约束扩散的情况，引入正弦缩放因子限制个体位置，减少因边界检查而出现的位于搜索边界的个体，提升优化器搜索性能。采用自适应t分布对当前最优个体施加扰动，增加种群多样性。在此基础上，使用增量Kriging代理模型辅助MGO进行优化计算，降低计算成本。结果:在22个不同基准测试函数上的实验结果表明，IKAEA-IMGO在求解效率和稳定性上均具有一定优势。将所提算法应用于无人机路径规划问题，进一步验证了其有效性。结论:基于MGO的增量Kriging辅助进化算法能够有效平衡全局探索和局部开发能力，适用于高效计算高维昂贵优化问题。
Abstract：Aims: To address dimensionality and computational bottlenecks in traditional evolutionary algorithms, this study proposes an incremental Kriging-assisted evolutionary algorithm based on the improved mountain gazelle optimizer(IKAEA-IMGO). Methods: A sine scaling factor was introduced to confine individual positions within the search space, thereby reducing boundary-dominated individuals caused by position constraints and improving the optimizer's search performance. Furthermore, to enhance population diversity, an adaptive t-distribution was adopted to perturb the current optimal individuals. Additionally, the incremental learning Kriging surrogate model was integrated to assist the MGO in optimization computations, effectively reducing computational costs. Results: Experimental comparisons with other surrogate-assisted evolutionary algorithms on 22 diverse benchmark functions demonstrated that IKAEA-IMGO exhibited superior efficiency and stability. Furthermore, its practical effectiveness was validated through an unmanned aerial vehicle(UAV) path planning application, highlighting its competence in solving real-world optimization challenges. Conclusions: The proposed incremental Kriging-assisted evolutionary algorithm based on the Improved Mountain Gazelle Optimizer can effectively address high-dimensional expensive optimization problems with rapid computational efficiency.
[1] HE C L,ZHANG Y,GONG D W,et al.A review of surrogate-assisted evolutionary algorithms for expensive optimization problems[J].Expert Systems with Applications,2023,217:119495.
[2] HAO H,ZHANG X Q,ZHOU A M.Expensive optimization via relation[J].IEEE Transactions on Evolutionary Computation,2025,29:1-15.
[3] 朱有钱，叶刚跃，叶伟荣，等.改进离散麻雀搜索算法在无人机电力巡检路径规划中的应用研究[J].中国计量大学学报，2025,36(1):61-67.ZHU Y Q,YE G Y,YE W R,et al.Application of an improved discrete sparrow search algorithm in the path planning of UAV electric power inspection[J].Journal of China University of Metrology,2025,36(1):61-67.
[4] ABDOLLAHZADEH B,GHAREHCHOPOGH F S,KHODADADI N,et al.Mountain gazelle optimizer:A new nature-inspired metaheuristic algorithm for global optimization problems[J].Advances in Engineering Software,2022,174:103282.
[5] 周睿，周坤，刘大亮，等.基于动态窗口法与人工势场法融合的差速机器人路径规划算法[J].中国计量大学学报，2023,34(4):556-562.ZHOU R,ZHOU K,LIU D L,et al.A differential robot path planning algorithm combining the dynamic window algorithm and the artificial potential field[J].Journal of China University of Metrology,2023,34(4):556-562.
[6] KLEIJNEN J P C.Kriging metamodeling in simulation:A review[J].European Journal of Operational Research,2009,192(3):707-716.
[7] TARAN N,IONEL D M,DORRELL D G.Two-level surrogate-assisted differential evolution multi-objective optimization of electric machines using 3-D FEA[J].IEEE Transactions on Magnetics,2018,54(11):1-5.
[8] YU M Y,LI X,LIANG J.A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization[J].Structural and Multidisciplinary Opti-mization,2020,61(2):711-729.
[9] RASMUSSEN C E,WILLIAMS C K I.Gaussian Processes for Machine Learning[M].Cambridge,Mass:MIT Press,2006:30.
[10] ZHAN D W,XING H L.A fast Kriging-assisted evolutionary algorithm based on incremental learning[J].IEEE Transactions on Evolutionary Computation,2021,25(5):941-955.
[11] WU R,HUANG H S,WEI J N,et al.An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement[J].Expert Systems with Applications,2023,215:119421.
[12] 刘志华，张冉，郝梦男，等.基于改进T分布烟花-粒子群算法的AUV全局路径规划[J].电子学报，2024,52(9):3123-3134.LIU Z H,ZHANG R,HAO M N,et al.AUV global path panning based on improved T-distribution fireworks-particle swarm optimization algorithm[J].Acta Ele-ctronica Sinica,2024,52(9):3123-3134.
[13] ZHU F,LI G S,TANG H,et al.Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems[J].Expert Systems with Applications,2024,236:121219.
[14] SUGANTHAN P N,HANSEN N,LIANG J J,et al.Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization[R].Singapore:Nanyang Technological University,2005.
[15] LIU B,ZHANG Q F,GIELEN G G E.A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems[J].IEEE Transactions on Evolutionary Computation,2014,18(2):180-192.
[16] WANG X J,WANG G G,SONG B W,et al.A novel evolutionary sampling assisted optimization method for high-dimensional expensive problems[J].IEEE Transactions on Evolutionary Computation,2019,23(5):815-827.
[17] YU H B,TAN Y,ZENG J C,et al.Surrogate-assisted hierarchical particle swarm optimization[J].Information Sciences,2018,454-455:59-72.
[18] SUN C L,JIN Y C,CHENG R,et al.Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems[J].IEEE Transactions on Evolutionary Computation,2017,21(4):644-660.
[19] 朱有钱.基于改进麻雀搜索算法的输电线路无人机巡检路径规划[D].杭州：中国计量大学，2025:18-25.ZHU Y Q.Path Planning for Transmission Line UAV Inspection Based on Improved Sparrow Search Algorithm[D].Hangzhou:China Jiliang University,2025:18-25.
基本信息:
中图分类号:V279;V249;TP18
引用信息:
[1]李益来,朱有钱,安斯光,等.基于改进山地瞪羚优化器的增量Kriging辅助进化算法研究及应用[J].中国计量大学学报,2025,36(04):562-572.
基金信息:
国家自然科学基金项目(No.52077203)
2025-12-15
2025-12-15