A hybrid Aquila Optimizer sine cosine Algorithm for Numerical Optimization

Abstract

To address the shortcomings of the Aquila optimizer algorithm (AO), this paper proposes a novel hybrid Aquila Optimizer sine cosine Algorithm(AO-SCA). Firstly, Singer chaotic mapping is used for initialization, so that the initial solution position distribution was more homogeneous, and increased the richness of the population. Secondly, in the exploration phase of AO, the concept of sine and cosine algorithm is integrated and the nonlinear sine learning factor is introduced to balance the local and global digging ability and accelerate the convergence speed. Finally, through the numerical experiment simulation of 8 benchmark functions, the results show that the optimization ability and convergence speed of the proposed algorithm is better.