基于多目标混合精英粒子群算法的三相整流系统控制器参数优化OA

To address the issues of strong subjectivity and low optimization efficiency in traditional empirical tuning methods for proportional integral(PI)controller parameters,this paper proposes a multi-objective hybrid elite particle swarm optimization(MOHEPSO)algorithm for parameter optimization of the three-phase rectifier system controller.This method integrates three core modules:an entropy-weighted method for elite particle selection(enhancing global search through information entropy minimization),a roulette wheel mechanism for high-quality particle optimization(guiding refined local search),and a dominance-based adaptive adjustment strategy(dynamically balancing global/local optimization capabilities).These components collectively construct an intelligent algorithmic framework with synergistic optimization characteristics.Simulation results demonstrate that,compared to conventional multi-objective particle swarm optimization(MOPSO)and particle swarm optimization(PSO),the proposed method achieves better performance in Matlab/Simulink simulations.Significant improvements are observed in key metrics including settling time,overshoot,and steady-state error,thereby validating its remarkable advantages in power electronics system optimization.