Adaptive Dynamic Aquila Optimization Incorporating with Improved Sine Cosine
Lang Huang, Hui Zhi Xu, Zhengbin Qin, Jin Yang · 2023
To address the issues of the Aquila Optimization (AO) easily falling into local optima, slow convergence speed, and low convergence accuracy, this paper proposes a fusion of adaptive dynamic Aquila Optimization incorporating with improved Sine Cosine (AO-SC). The AO-SC algorithm combines the adaptive weight mechanism and the nonlinearly decreasing search factor of the sine cosine algorithm to perform secondary optimization, effectively improving convergence accuracy and speed, and escaping from local optima. Additionally, the Cauchy-Gaussian mutation operator is also utilized to enhance the proposed AO-SC algorithm's global search capability. Finally, the introduction of nonlinear adaptive weight factors and the combination of archimedean spiral pattern with the variable step size feature of the original AO algorithm generate new local solutions to improve the local search capability and search accuracy of the AO algorithm. Through testing and comparison with seven other algorithms on ten benchmark functions, the final experimental results demonstrate that, the proposed AO-SC algorithm has significant advantages in terms of convergence speed, accuracy, and stability.