An experimental analysis of several new swarm intelligence optimization algorithms

With the rapid development of computer technology, algorithm technology is also constantly innovating. The swarm intelligence optimization algorithm has gradually become the most important class of algorithms because of its simple and easy-to-implement characteristics. It has been widely used in optimization problems and provides new ideas for solving complex engineering problems. The latest swarm intelligence algorithms proposed at home and abroad are selected for analysis, including Arithmetic Optimization Algorithm (AOA), Wild Horse Optimization (WHO), Dingo Optimization Algorithm (DOA), and Artificial Hummingbird Algorithm (AHA). The paper uses 12 general benchmark functions and part of CEC2017 test functions for experimental simulation, and further compares the experimental overall performance of these algorithms in phrases of convergence speed, accuracy, and stability. Finally, the future development of the swarm intelligence algorithm is discussed.