1. 沈阳航空航天大学 计算机学院,辽宁,沈阳,110136
2. 沈阳航空航天大学 机构 能源与环境学院,辽宁,沈阳,110136
纸质出版:2017
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郭振洲, 王平, 马云峰, 王琦, 拱长青. 基于自适应权重和柯西变异的鲸鱼优化算法[J]. 微电子学与计算机, 2017, 34(9): 20-25.
GUO Zhen-zhou, WANG Ping, MA Yun-feng, et al. Whaleoptimization Algorithm Based on Adaptive Weight and Cauchy Mutation[J]. 2017, 34(9): 20-25.
为了改进鲸鱼算法(WOA)容易陷入局部最优和收敛精度低的问题
提出了基于自适应权重和柯西变异的鲸鱼算法(WOAWC).首先通过柯西逆累积分布函数方法对鲸鱼位置进行变异
提高了鲸鱼算法的全局搜索能力
避免了陷入局部最优.另外通过自适应权重的方法改进了鲸鱼算法的局部搜索能力
提高了收敛精度; 实验结果表明
改进的算法和原鲸鱼算法、遗传算法、粒子群算法相比
收敛精度和算法稳定性上都要优于其它算法.
In order to improve the problem that the whale algorithm (WOA) is easy to fall into the local optimum and the convergence accuracy is low
a whale algorithm based on adaptive weight and Cauchy mutation is proposed (WOAWC). Firstly
the variation of the whales position is modified by the Cauchy inverse cumulative distribution function method
which improves the global search ability of the whale algorithm and avoids the local optimization. In addition
the local search ability of the whale algorithm is improved by the adaptive weighting method
and the convergence accuracy is improved. The experimental results show that the improved algorithm is superior to the original whale algorithm
genetic algorithm and particle swarm optimization
Convergence accuracy and algorithm stability are better than other algorithms.
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赵志刚, 黄树运, 王伟倩. 基于随机惯性权重的简化粒子群优化算法[J]. 计算机应用研究, 2014, 30(2): 361-363. DOI: 361
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赵志刚, 林玉娇, 尹兆远. 基于自适应惯性权重的均值粒子群优化算法[J]. 计算机工程与科学, 2016, 38(3): 501-506. DOI: 501
杜晓昕, 张剑飞, 郭媛, 等. 基于柯西-高斯动态消减变异的果蝇优化算法研究[J]. 计算机工程与科学, 2016, 38(6): 1171-1176. DOI: 1171
傅小利, 顾红兵, 陈国呈, 等. 基于柯西变异粒子群算法的永磁同步电机参数辨识[J]. 电工技术学报, 2014, 29(5): 127-131. DOI: 127
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