南京信息工程大学学报2026，Vol.18Issue(2)：231-246,16.DOI:10.13878/j.cnki.jnuist.20250327003
融合逐维高斯变异的改进白鲸优化算法及其应用
Improved beluga whale optimization combining dimension-by-dimension Gaussian mutation and its applications
摘要
Abstract
To address the imbalance between exploration and exploitation,slow convergence speed,premature con-vergence,and poor local optima escape in the Beluga Whale Optimization(BWO),an Improved BWO algorithm combining dimension-by-dimension Gaussian mutation(IBWO)is proposed.Firstly,a dynamic parameter strategy is adopted to adjust the balance factor,achieving a better balance between exploration and exploitation.Secondly,the current-to-rand differential mutation operator is introduced to enhance the algorithm's exploration capability.Then,elite leadership strategies are incorporated to accelerate the convergence speed.Finally,dimension-by-dimension Gaussian mutation is applied to the current optimal solution,based on the positions of both the current optimal and worst solutions,thereby enhancing the algorithm's ability to escape local optima.To validate the performance of the improved algorithm,it is compared with seven other metaheuristic algorithms on the Congress on Evolutionary Computation's(CEC)2017 test set,the experimental results demonstrate that IBWO exhibits superior optimization capabilities compared to other algorithms.Applied IBWO to three engineering problems,IBWO shows promising per-formance in solving complex real-world optimization problems.关键词
白鲸优化算法/动态参数/逐维高斯变异/群体智能算法/最优化/工程应用Key words
beluga whale optimization(BWO)/dynamic parameters/dimension-by-dimension Gaussian mutation/swarm intelligence algorithm/optimization/engineering applications分类
信息技术与安全科学引用本文复制引用
徐烁,邹德旋,宋博,胡俊杰,张响..融合逐维高斯变异的改进白鲸优化算法及其应用[J].南京信息工程大学学报,2026,18(2):231-246,16.基金项目
国家自然科学基金(62373173) （62373173）
