An elitist whale optimization algorithm with the nonlinear parameter: Algorithm and application

Yajing Zhang, Guoxu Zhang
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Abstract

To address the problem that the whale optimization algorithm tends to fall into the local optimum and fails to maintain a balance between exploration and exploitation, an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed in this paper. An elitist strategy based on the random Cauchy mutation is used in the shrinking encircling mechanism to increase the chance of escaping the local optimum. Cleverly, the strategy is to generate mutation solutions based on the random Cauchy mutation, after which the better population is selected to proceed to the next iteration. Then, a nonlinear parameter is used in the logarithmic spiral mechanism to balance exploration and exploitation. Various numerical optimization experiments are performed based on the IEEE CEC2020 benchmark suite and compared with eleven other algorithms. The results show that EWOANP outperforms most competitors in numerical optimization. Finally, the backpropagation neural network is optimized by EWOANP to build a prediction model for the sulfur content in the molten iron. The experimental results based on production data indicate that the proposed prediction model has a relatively small fluctuation in errors. Compared to the other seven competitors, the proposed model has a better prediction performance with and =0.916619.
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带有非线性参数的精英鲸鱼优化算法:算法与应用
针对鲸鱼优化算法容易陷入局部最优、无法保持探索与开发平衡的问题,本文提出了一种带有非线性参数的精英鲸鱼优化算法(EWOANP)。在收缩包围机制中使用了基于随机考奇突变的精英策略,以增加逃离局部最优的机会。巧妙的是,该策略是在随机考奇突变的基础上产生突变解,然后选择较好的种群进行下一次迭代。然后,在对数螺旋机制中使用一个非线性参数来平衡探索和开发。基于 IEEE CEC2020 基准套件进行了各种数值优化实验,并与其他 11 种算法进行了比较。结果表明,EWOANP 在数值优化方面优于大多数竞争对手。最后,EWOANP 对反向传播神经网络进行了优化,以建立铁水中硫含量的预测模型。基于生产数据的实验结果表明,所提出的预测模型误差波动相对较小。与其他七个竞争者相比,所提出的预测模型具有更好的预测性能,和 =0.916619。
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