Aquila optimizer with dynamic group strategy for global optimization tasks

The Aquila optimizer (AO) is an efficient method for solving global optimization problems. However, the evolution of each individual learns from experience in the same group, which can easily fall into local optima. Therefore, this paper adopts the dynamic grouping strategy (DGS) of the population and proposes an improved AO algorithm to solve the global optimization problem. Different from the original AO algorithm, the DGSAO algorithm only evolves the individuals with the worst fitness in each group each time, which increases the diversity of the population. In order to verify the effectiveness of the algorithm, we tested it on 23 benchmark functions, among which the dimensions of F1$$ {F}_1 $$ to F13$$ {F}_{13} $$ are 30, 100, and 200 dimensions. The experimental results show that the DGSAO algorithm is an effective method for solving global optimization problems. At the same time, we also conduct experiments on two engineering design problems, and the results show that the DGSAO algorithm can obtain a competitive result.

Automated summary

This paper introduces an improved algorithm of the Aquila optimizer, called Dynamic Grouping Strategy (DGS). By evolving the individuals with the worst fitness in each group, and testing it on multiple benchmark functions and engineering design problems, it proves the effectiveness of the algorithm in solving global optimization problems.