《计算机应用研究》|Application Research of Computers

一种改进的鲸鱼优化算法

Improved whale optimization algorithm

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作者 武泽权,牟永敏
机构 北京信息科技大学 a.网络文化与数字传播北京市重点实验室;b.计算机学院,北京 100101
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文章编号 1001-3695(2020)12-020-3618-04
DOI 10.19734/j.issn.1001-3695.2019.09.0536
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
基金项目 北京市自然科学基金资助项目(Z160002)
网络文化与数字传播北京市重点实验室开放课题资助项目(5221935409)
本文URL http://www.arocmag.com/article/01-2020-12-020.html
英文标题 Improved whale optimization algorithm
作者英文名 Wu Zequan, Mu Yongmin
机构英文名 a.Beijing Key Laboratory of Internet Culture & Digital Dissemination Research,b.School of Computer Science,Beijing Information Science & Technology University,Beijing 100101,China
英文摘要 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 lear-ning 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.
英文关键词 whale optimization algorithm; quasi-reverse learning; nonlinear convergence factor; adaptive weight; random differential variation
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收稿日期 2019/9/13
修回日期 2019/10/30
页码 3618-3621
中图分类号 TP301.6
文献标志码 A