Purpose

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

Design/methodology/approach

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

Findings

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

Originality/value

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

中文翻译：

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基于征税飞行和基于对立学习的改进麻雀搜索算法

目的

麻雀搜索算法（SSA）是一种新颖的全局优化方法，但容易陷入局部优化，导致其搜索精度和稳定性较差。本研究的目的是提出一种基于 LOSSA 策略的改进 SSA 算法，称为征费飞行和基于反对的学习 (LOSSA)。LOSSA 具有更好的搜索精度、更快的收敛速度和更强的稳定性。

设计/方法/方法

为进一步提升算法的优化性能，在原始SSA的生产者搜索过程中引入了Levy飞行操作，以增强算法跳出局部最优的能力。基于对立的学习策略为 SSA 生成更好的解，有利于加快算法的收敛速度。一方面，通过一组基于经典基准函数的数值实验来评估 LOSSA 的性能。另一方面，支持向量机（SVM）的超参数优化问题也被用来检验LOSSA解决实际问题的能力。

发现

首先，通过Wilcoxon符号秩检验验证了两种改进方法的有效性。其次，数值实验的统计结果表明，与原始算法和其他自然启发式算法相比，LOSSA 有显着的改进。最后，证明了 LOSSA 在解决机器学习算法超参数优化问题中的可行性和有效性。

原创性/价值

本文提出了一种基于 LOSSA 的改进 SSA。实验结果表明，LOSSA的整体性能是令人满意的。与SSA等自然启发式算法相比，LOSSA具有更好的搜索精度、更快的收敛速度和更强的稳定性。而且，LOSSA在SVM模型的超参数优化中也表现出了很好的优化性能。