An improved Algorithm Optimization Algorithm based on RungeKutta and golden sine strategy

To overcome the shortcomings of the algorithm optimization algorithm (AOA), such as its slow convergence speed and poor global search ability, an improved AOA based on RungeKutta and golden sine strategy (RGAOA) is proposed. In this algorithm, the improved r1 based on the sine factor is proposed and compared with the math optimizer accelerated (MOA) values for each iteration. In this way the weighting of the exploration phase and the exploitation phase of the optimization process is reconstructed. Then, the gold sine strategy is used to guide individuals to approach the optimal solutions. After obtaining the current optimal solution, the quality of the current optimal solution is further enhanced by the Enhanced Solution Quality (ESQ) of the RungeKutta optimizer (RUN). Then, twenty benchmark test functions, the CEC2017, CEC2019 test functions (2017 and 2019 IEEE Congress on Evolutionary Computation test functions) and the practical engineering application problems were selected to test the overall performance of the improved algorithm, and the results were compared with other algorithms and other improved versions. The experimental results show an 89.19% improvement in convergence speed, a 90.07% improvement in convergence accuracy and a 67.99% improvement in stability compared to AOA.