XIE Junfei,ZHANG Haiqing,LI Daiwei,et al.Improved Whale Optimization Algorithm based on Inertia Weights[J].Journal of Chengdu University of Information Technology,2025,40(05):600-604.[doi:10.16836/j.cnki.jcuit.2025.05.005]
基于惯性权重的改进鲸鱼优化算法
- Title:
- Improved Whale Optimization Algorithm based on Inertia Weights
- 文章编号:
- 2096-1618(2025)05-0600-05
- Keywords:
- population intelligent optimization algorithm; whale optimization algorithm; inertia weights; simulated annealing
- 分类号:
- TP181
- 文献标志码:
- A
- 摘要:
- 作为一种种群智能优化方法,鲸鱼优化算法以其简易机制、少量参数和强大寻优能力著称,但也面临收敛速度慢和易陷入局部最优解的挑战。为此,提出一种融合惯性权重的改进鲸鱼优化算法WWOA,通过添加惯性权重因子平衡算法全局探索和局部开发能力,加快收敛速度; 同时在算法中引入模拟退火策略,通过一定概率接受劣质解,优化算法全局搜索能力。在12个单峰、多峰测试函数上,共进行50次实验,计算平均值与标准差,并与其他模型对比,结果表明:WWOA算法在7个测试函数上的平均值最小,在2个测试函数上的平均值为次小,证明算法提出的有效性。
- Abstract:
- As a population intelligence optimization method,the whale optimization algorithm is known for its simple mechanism,small number of parameters,and strong optimization ability.However,it also faces the challenges of slow convergence speed and easy to fall into local optimal solutions.To this end,this paper proposes an improved whale optimization algorithm WWOA fused with inertia weights,which accelerates the convergence speed by adding the global exploration and local development capabilities of the inertia weight factor balancing algorithm.At the same time,the simulated annealing strategy is introduced into the algorithm to accept inferior solutions through a certain probability to optimize the global search ability of the algorithm.A total of 50 experiments were carried out on 12 single-peak and multi-peak test functions,the mean and standard deviation were calculated,and compared with other models,the experimental results showed that the WWOA algorithm had the smallest result on 7 test functions,and the second smallest result on 2 test functions,which proved the effectiveness of the proposed algorithm.
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相似文献/References:
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HUANG Yan,WU Zezhong.An Improved Whale Algorithm based on Lévy Flight[J].Journal of Chengdu University of Information Technology,2021,36(05):24.[doi:10.16836/j.cnki.jcuit.2021.01.005]
备注/Memo
收稿日期:2024-03-24
基金项目:中国气象局“揭榜挂帅”科技资助项目(CMAJBGS202302)
通信作者:李代伟.E-mail:ldwcuit@cuit.edu.cn