Abstract:

With the development of computer technology, algorithm technology is constantly and alternately being updated. In recent years, swarm intelligence algorithm has become more and more popular and received extensive attention and research, and has made progress in such fields as machine learning, process control and engineering prediction. Swarm intelligence optimization algorithm is a biological heuristic method, which is widely used in solving optimization problems. The traditional swarm intelligence algorithm provides some new ideas for solving some practical problems, but it also exposes some shortcomings in some experiments. In recent years, many scholars have proposed many new types of intelligent optimization algorithms. This paper selects the more typical swarm intelligence algorithms at home and abroad in recent years, such as Bat Algorithm（BA）, Grey Wolf Optimization Algorithm（GWO）, Dragonfly Algorithm（DA）, Whale Optimization Algorithm（WOA）, Grasshopper Optimization Algorithm（GOA） and Sparrows Search Algorithm（SSA）, and further compares the experimental performance of these algorithms and the development potential by 22 standard CEC test functions from the convergence speed and accuracy, stability and so on, and the refinement analysis is carried out to compare and analyze the relevant improvement methods. Finally, the characteristics of swarm intelligence optimization algorithm are summarized and its development potential is discussed.

Key words: swarm intelligence optimization algorithm, optimization problem, biological heuristic algorithm, sparrows search algorithm, whale optimization algorithm

摘要：

随着计算机技术的发展，算法技术也在不断交替更新。近年来，群体智能算法受到了广泛的关注和研究，并在诸如机器学习、过程控制、工程预测等领域取得了进展。群智能优化算法属于生物启发式方法，广泛应用在解决最优化问题上，传统的群智能算法为解决一些实际问题提供了新思路，但是也在一些实验中暴露出不足。近年来，许多学者相继提出了很多新型群智能优化算法，选取了最近几年国内外提出的比较典型的群智能算法，蝙蝠算法（Bat Algorithm，BA）、灰狼优化算法（Grey Wolf Optimization，GWO）、蜻蜓算法（Dragonfly Algorithm，DA）、鲸鱼优化算法（Whale Optimization Algorithm，WOA）、蝗虫优化算法（Grasshopper Optimization Algorithm，GOA）和麻雀搜索算法（Sparrow Search Algorithm，SSA），并进一步通过22个标准的CEC测试函数从收敛速度、精度和稳定性等方面对比了这些算法的实验性能，并对比分析了其相关的改进方法。最后总结了群智能优化算法的特点，探讨了其今后的发展潜力。

关键词: 群智能优化算法, 优化问题, 生物启发式算法, 麻雀搜索算法, 鲸鱼优化算法