关键词: 蝴蝶优化算法, 樽海鞘群算法, 邻域重心反向学习, 混合算法, 惯性权重, 标准测试函数

Abstract:

Aiming at the problems of slow convergence and premature convergence to local solutions of Butterfly Optimization Algorithm （BOA）， a neighborhood centroid opposition-based learning based Hybrid Salp Swarm and Butterfly Optimization Algorithm （HSSBOA） was proposed. Firstly， Salp Swarm Algorithm （SSA） was introduced into BOA to make the algorithm quickly deal with the local search stage， and update the population position. As a result， the optimization process was completed more effectively to avoid the algorithm falling into the local optimum. Then， neighborhood centroid opposition-based learning was introduced to make the algorithm search accurately in a small range of the neighborhood， increasing the accuracy of the algorithm. Finally， dynamic switching probability was introduced to improve the global and local proportion in the search， which accelerated the convergence of the algorithm. With ten benchmark functions selected for testing， HSSBOA was compared with several advanced algorithms from convergence accuracy， high-dimensional data， convergence speed， Wilcoxon rank sum test and Mean Absolute Error （MAE）. Research results show that HSSBOA achieves better results than other algorithms. In addition， the ablation experiment was used to further verify that the proposed improvements are positive. The performance on instance problems show that HSSBOA searches the optimal solution more effectively when solving constrained complex problems compared with other methods. It can be seen that HSSBOA has some advantages in optimization accuracy， stability and convergence efficiency， and it is able to solve complex practical problems.

Key words: Butterfly Optimization Algorithm (BOA), Salp Swarm Algorithm (SSA), neighborhood centroid opposition-based learning, hybrid algorithm, inertia weight, benchmark function