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

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