Abstract: This paper proposes an arithmetic optimization algorithm that integrates the sine-cosine strategy to address the problems of low solution accuracy, slow convergence speed, and easy fall into local optima in arithmetic optimization algorithms. The algorithm adaptively adjusts the math optimizer acceleration (MOA) accelerator function based on the change information of individual fitness, balancing the global exploration and local exploitation abilities of the algorithm. The improved sine-cosine algorithm is introduced into the local development stage of the algorithm, increasing the population diversity in the later iterations, avoiding the algorithm from falling into local optima, and effectively improving the solution accuracy and convergence speed of the algorithm. Simulation experiments on 14 benchmark test functions show that the improved algorithm has significant improvements in solution accuracy, convergence speed, and robustness. Finally, the improved algorithm is applied to the optimization of support vector machine (SVM) parameters, and a student knowledge level prediction model is established, which further verifies the practicality and superiority of the algorithm.

Key words: arithmetic optimization algorithm, adaptive, sine-cosine algorithm, function optimization