Arithmetic Optimization Algorithm Improved by Multi-Strategy Fusion

To address the issues of slow convergence, low accuracy, and vulnerability to local optima in the Arithmetic Optimization Algorithm (AOA), this paper introduces the multi-strategy fused improved Arithmetic Optimization Algorithm (CLSAOA). Initially, the Circle chaotic mapping is utilized for population initialization, thereby augmenting diversity and balance within the initial population. Then, the lens imaging opposition-based learning strategy is incorporated into the optimization phase of AOA to improve the algorithm's exploration capabilities and its ability to escape local optima, thereby enhancing the convergence accuracy and speed. Finally, a sine-cosine strategy is added to perturb the particle positions, further improving the algorithm's convergence accuracy. Simulation experiments compare with other improved AOA algorithms, the original AOA algorithm, and other metaheuristic algorithms using international benchmark test functions for optimization. The findings indicate that the CLSAOA algorithm exhibits superior performance in terms of both convergence precision and speed.