基于余弦控制因子和多项式变异的鲸鱼优化算法

针对基本鲸鱼优化算法(Whale optimization algorithm,WOA)在求解最优解不在原点附近的目标函数时存在收敛精度低、易陷入局部最优解的缺陷,提出一种基于余弦控制因子和多项式变异的鲸鱼优化算法(CPWOA).所提算法中控制参数按照余弦曲线变化,并加入同步余弦惯性权值,使得在迭代前期减缓收敛速度以进行充分的全局探索,而在迭代后期加速收敛以提高算法精度;同时,对最佳鲸鱼位置引入多项式变异,以增强算法跳出局部最优解的能力.将所提算法对多个shifted单峰、多峰和固定维测试函数进行求解,实验结果表明,与基本WOA、EHO、GWO、SCA、MBO以及其他改进型WOA算法相比,CPWOA对绝大多数测试函数的求解有更高的精度和稳定性.用非参数估计方法对计算结果进行差异显著性统计检验,表明CPWOA算法的显著性更优.

Whale optimization algorithm based on cosine control factor and polynomial mutation

The basic whale optimization algorithm(WOA) has the defects of low convergence, getting easily trapped into local optima, and being difficult to keep balance in exploration and exploitation when solving the shifted functions whose optimum are not at the near origin. A whale optimization algorithm based on cosine control factors and polynomial mutation(CPWOA) is proposed to solve the mentioned defects. In this algorithm, the control parameter is changed as a cosine curve, and a synchronous cosine inertia weight is added to slow down the convergence speed early in the iteration of the algorithme thus improve exploration, and to accelerate the convergence in the later iteration thus improve the accuracy of the exploitation. And polynomial mutation is joined in the optimum whale location to enhance the ability of jumping out of local optimal solutions. By the experiments on multiple shifted benchmark functions such as unimodal, multimodal, and fixed-dimension multimodal, the proposed strategy outperforms the WOA, the EHO, the GWO, the SCA, the MBO and other improved WOAs on solution accuracy and stability. The non-parametric statistical test is carried out to show the significance of the difference of the proposed method.