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鲸鱼优化算法(WOA)在处理高维度数据和含有多个局部最优解的问题时，存在着一定的局限性，如易陷入局部最优、收敛速度慢和参数调整不够灵活。于是提出了一种改进的WOA算法，设计了一种非线性收敛因子用于替代原算法中的线性收敛因子，提高算法的收敛速度；其次，引入一种自适应权重机制，避免单一惯性权重可能导致的过早收敛或振荡问题；然后，融合黄金分割算法提高鲸鱼群体的多样性和全局搜索能力。最后，将所提出算法在8个测试函数以及机器人路径规划等方面与其他算法进行实验对比，实验结果表明，所提出算法得到的效果更好。
Abstract：The Whale Optimization Algorithm(WOA) is constrained in its ability to process high-dimensional data and problems with multiple local optimal solutions. These limitations include a proclivity to converge on local optima, a slow convergence speed, and an insufficient degree of flexibility in parameter adjustment. An enhanced version of the WOA algorithm is presented. Firstly, a nonlinear convergence factor was designed to replace the linear convergence factor in the original algorithm, thereby improving the convergence speed of the algorithm. Secondly, an adaptive weight mechanism was introduced to circumvent premature convergence or oscillation issues that may arise from a single inertia weight. Subsequently, the Golden Section algorithm is integrated with the objective of enhancing the diversity of whale groups and the capacity for global search. Finally, the proposed algorithm is evaluated in comparison with other algorithms on the basis of eight test functions and robot path planning. The experimental results demonstrate that the proposed algorithm exhibits superior performance.
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基本信息:
DOI：10.19323/j.issn.1673-6524.202409011
中图分类号:TP242;TP18
引用信息:
[1]曾广财,叶军,宋苏洋,等.一种改进的鲸鱼优化算法在机器人路径规划中的应用[J].火炮发射与控制学报,2025,46(05):7-14.DOI:10.19323/j.issn.1673-6524.202409011.
基金信息:
国家自然科学基金项目(62166027); 江西省教育厅科技项目(GJJ211920)