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一种改进的鲸鱼优化算法
引用本文:武泽权,牟永敏.一种改进的鲸鱼优化算法[J].计算机应用研究,2020,37(12):3618-3621.
作者姓名:武泽权  牟永敏
作者单位:北京信息科技大学 网络文化与数字传播北京市重点实验室,北京 100101;北京信息科技大学 计算机学院,北京 100101
基金项目:北京市自然科学基金;网络文化与数字传播北京市重点实验室开放基金
摘    要:针对鲸鱼优化算法(whale optimization algorithm ,WOA)容易陷入局部最优和收敛精度低的问题进行了研究,提出一种改进的鲸鱼优化算法(IWOA)。该算法通过准反向学习方法来初始化种群,提高种群的多样性;然后将线性收敛因子修改为非线性收敛因子,有利于平衡全局搜索和局部开发能力;另外,通过增加自适应权重改进鲸鱼优化算法的局部搜索能力,提高收敛精度;最后,通过随机差分变异策略及时调整鲸鱼优化算法,避免陷入局部最优。实验选取九个基准函数,所有算法均迭代30次,结果表明:改进的鲸鱼优化与原鲸鱼优化算法以及五种改进的鲸鱼优化算法相比,其均值和标准差均优于其他算法,收敛曲线也优于其他大多数算法。说明改进的鲸鱼优化算法收敛精度和算法稳定性最佳,收敛速度较其他大多数改进的鲸鱼优化算法明显加快。

关 键 词:鲸鱼优化算法  准反向学习  非线性收敛因子  自适应权重  随机差分变异
收稿时间:2019/9/13 0:00:00
修稿时间:2020/10/30 0:00:00

Improved whale optimization algorithm
wuzequan and muyongmin.Improved whale optimization algorithm[J].Application Research of Computers,2020,37(12):3618-3621.
Authors:wuzequan and muyongmin
Affiliation:Beijing key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University,
Abstract:Aiming at the problem that the WOA was easy to fall into local optimum and low convergence precision, this paper proposed an improved whale optimization algorithm(IWOA). The algorithm initialized the population by quasi-reverse learning methods and improved the diversity of the population. Then the algorithm modified the linear convergence factor to a nonlinear convergence factor, which was beneficial to balance the global search ability and local development ability. In addition, the algorithm improved the local search ability of the whale optimization algorithm by increasing the adaptive weight and improved convergence precision. Finally, the algorithm adjusted the whale optimization algorithm in time by a random differential mutation strategy to avoid falling into the local optimum. It selected nine benchmark functions in the experiment, and iterated all the algorithms 30 times. The improved whale optimization algorithm compared to the original whale optimization algorithm and five improved whale optimization algorithms, the results show that the mean and standard deviation of the algorithm are better than other algorithms, the convergence curve of the algorithm is also superior to most other algorithms. It shows that the improved whale optimization algorithm has the best convergence accuracy and algorithm stability, and the convergence speed is significantly faster than most other improved whale optimization algorithms.
Keywords:whale optimization algorithm  quasi-reverse learning  nonlinear convergence factor  adaptive weight  random differential variation
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