Bidirectional DCDC Converter Based on Improved Slime Mold Algorithm PID Control

This article mainly studies the application of improved slime mold algorithm in PID tuning. This article proposes a Slime mold optimization algorithm (ESMA) that combines elite reverse greedy learning and Levy flight. The algorithm first optimizes the initial population through elite reverse learning, improving the diversity of the population while accelerating the initial iteration effect. In the later stage of the algorithm, Levy flight strategy is introduced to perform small-scale random search at the current optimal position. Through the simulation of multiple test functions and MATLAB/Simulink circuits, the results show the superiority of the improved slime mold algorithm in PID parameter tuning in the design of bidirectional DCDC converters.