Hybridizing remora and aquila optimizer with dynamic oppositional learning for structural engineering design problems

Abstract

To solve global optimization problems, the Aquila Optimizer (AO) algorithm was created recently and is based on the hunting habits of Aquila birds. The Remora Optimization Algorithm (ROA) is combined with a novel Aquila optimizer in this study to create a hybrid version that generates new local solutions based on the best available ones, thereby improving searchability. Additionally, the implementation of dynamic oppositional-based learning (DOL) techniques facilitates both the exploration and exploitation of a search field while preserving an appropriate balance between them. Designated RODAO, is the proposed algorithm. The fundamental characteristic of the proposed approach is the use of Remora's ability to prevent premature convergence and local search problems, as well as the DOL strategy to preserve high-quality solutions and variety among the RODAO's solutions. In order to assess these competencies in RODAO, the IEEE CEC 2017 benchmark functions as well as a traditional set of well-known benchmark functions have been used. The robustness and efficiency of the method are guaranteed by a number of performance measurements used on RODAO, including statistical tests and convergence graphs. Three popular engineering optimization issues are also solved in the paper using the suggested RODAO technique. The analysis and numerical experiments show that real-world optimization issues can be successfully solved by the proposed algorithm or RODAO.