Chaotic Aquila Optimization algorithm for solving global optimization and engineering problems

Abstract

The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting behavior of Aquila birds in nature. However, in complex optimization problems, the AO has shown a slow convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, a hybrid with AO and twelve chaotic maps has been proposed to adjust its main parameter. This new mechanism, namely the Chaotic Aquila Optimization (CAO) algorithm, is employed with chaotic maps with the AO algorithm. The proposed chaotic AO (CAO) approach takes seriously a variety of chaotic maps while setting the main AO parameter, which helps in managing exploration and exploitation. To validate the performance of the CAO algorithm, estimates for CEC 2005 and CEC 2022 test functions and the first chaotic map results are compared with the AO algorithm to select the best results of the CAO algorithm, and then CAO results are compared with nine popular optimization algorithms such as FFA, AVOA, MGO, AGTO, SSA, GWO, MVO, SCA, TSA, and AO. Moreover, statistical analyses such as the Wilcoxon rank-sum test and the t-test are performed to analyze the significant difference between the proposed CAO and other algorithms. Furthermore, the proposed CAO has been employed to solve six real-world engineering problems. The results demonstrate the CAO’s superiority and capability over other algorithms in solving complex optimization problems. The results demonstrate that CAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.

Keywords

• Aquila optimization algorithm
• Chaotic maps
• Chaotic Aquila optimization algorithm
• Test functions
• Real-life problems