Hybrid particle swarm optimization with differential evolution for numerical and engineering optimization

In this paper, a hybrid particle swarm optimization (PSO) algorithm with differential evolution (DE) is proposed for numerical benchmark problems and optimization of active disturbance rejection controller (ADRC) parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world optimization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.

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