摘要
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 Cauehy mutation is proposed （WOAWC）. Firstly, the variation of the whale＇s 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.
出处
《微电子学与计算机》
CSCD
北大核心
2017年第9期20-25,共6页
Microelectronics & Computer
关键词
鲸鱼算法
自适应权重
柯西变异
遗传算法
粒子群算法
whale algorithm
adaptive weight
cauchy mutation
genetic algorithms
particle swarm optimization
