The seeker optimization algorithm (SOA) is a heuristic random search algorithm that has fast convergence. SOA has good robustness and can simulate the human search behavior. In the traditional SOA, global and local searches are adjusted by linearly decreasing the inertia weights; however, this algorithm has poor local search ability and adaptive adjustment ability in the initial iteration stage. The traditional SOA also easily falls in the local optimum. To overcome the shortcomings of the SOA, we propose an SOA based on the dynamic adaptive search step (hereinafter DASSS–SOA). This algorithm dynamically adjusts the inertia weight by introducing the objective function value of the current population to enhance the local search ability of the algorithm. The effectiveness of DASSS–SOA was verified by testing and analyzing the typical functions. Finally, to optimize the weight of the wavelet neural network (WNN) and predict the network traffic, the DASSS–SOA and WNN were combined (hereinafter DASSS–SOA–WNN). The simulation results showed that DASSS–SOA–WNN had a higher prediction accuracy than the WNN and SOA–WNN prediction models.

搜寻器优化算法（SOA）是一种具有快速收敛性的启发式随机搜索算法。SOA具有良好的鲁棒性，并且可以模拟人工搜索行为。在传统的SOA中，通过线性减小惯性权重来调整全局和局部搜索。然而，该算法在初始迭代阶段具有较弱的局部搜索能力和自适应调整能力。传统的SOA也容易陷入局部最优状态。为了克服SOA的缺点，我们提出了一种基于动态自适应搜索步骤的SOA（以下称为DASSS-SOA）。该算法通过引入当前总体的目标函数值来动态调整惯性权重，以增强算法的局部搜索能力。通过测试和分析典型功能，验证了DASSS-SOA的有效性。最后，为了优化小波神经网络（WNN）的权重并预测网络流量，将DASSS–SOA和WNN组合在一起（以下简称DASSS–SOA–WNN）。仿真结果表明，DASSS–SOA–WNN的预测精度高于WNN和SOA–WNN的预测模型。