Abstract: Aiming at the defects of differential evolution algorithm,such as loss of population diversity and premature convergence when dealing with multimodal complex optimization problems,a differential evolution based on adaptive parameter control and self-guided perturbation(APE-DE)is proposed.First,it designs a self-guided perturbation compensating scheme to guide its search direction by considering the individual’s spatial position,effectively avoiding the dilemma of falling into the local optimum.Second,the algorithm also develops an extreme dimension exchange strategy,which evaluates population diversity from multiple dimensions and implements related different diversity enhancement schemes.Finally,the algorithm proposes an adaptive parameter control strategy that combines information from wavelet basis functions and fitness distribution deviations to capture the dynamic changes in population fitness in real time and adjust the algorithm parameters accordingly.To verify the performance of APE-DE,experiments are conducted on the widely used IEEE CEC2017 data set to validate the effectiveness of the algorithm in multimodal and complex environments.Experimental results show that compared with eight advanced differential evolution variants,APE-DE exhibits significant advantages in both convergence accuracy and convergence speed.Furthermore,to evaluate the effectiveness of APE-DE in solving real-world problems,the proposed algorithm is applied to the parameter identification problem of photovoltaic models.

Key words: Differential evolution, Parameter adaptation, Self-guided disturbance compensation, Extreme dimension exchange, Diversity enhancement