Multi-Strategy Co-Ordination Aquila Optimizer For Optimization of Mechanical Structure

Aquila Optimizer(AO) is a recently proposed metaheuristic optimization algorithm. Although AO has significant global exploration capabilities, its local exploitation capabilities are weak. To address the problem of AO's tendency to experience sluggish convergence and local optimum, a Multi-Strategy Co-ordination Aquila Optimizer (MCAO) algorithm is proposed in this paper. The population is initially initialized using Bernoulli's chaotic map. Gaussian difference variation is applied for updating the population during iteration. The adaptive weight strategy based on Tent chaotic sequence is used to speed up the convergence speed of the algorithm. Finally, the parabolic foraging strategy is applied to escape from the local optimum. The proposed MCAO algorithm was tested using part of the CEC2021 test function. The findings demonstrate that MCAO outperforms AO in terms of convergence accuracy and convergence rate. Finally, MCAO is used in two industrial engineering problems, through experimental results, It has been proven that the MCAO is very applicable to solving real-world problems.