摘要
针对差分进化算法在应对多模态复杂优化问题时面临种群多样性丧失和过早收敛的缺陷,提出了一种基于自扰动和极性维度交互的自适应差分进化算法(Adaptive Differential Evolution Based on Self-guided Perturbation and Extreme Dimension Exchange,APE-DE)。首先,设计了一种自扰动补偿策略,通过个体的空间位置来引导其搜索方向,有效避免了算法易陷入局部最优的困境。然后,提出了一种极性维度交互策略,用于提升算法多样性,一旦种群被检测出停滞,将启动相应的增强方案。最后,提出了一种自适应参数控制策略,通过小波基函数和适应度分布偏差信息实时捕捉种群适应度的变化,并据此动态调整算法参数。为了验证APE-DE的性能,在被广泛使用的IEEE CEC2017数据集上进行了实验,以验证算法面对多模态及复杂测试环境下的性能。实验结果表明,与8种最先进的差分进化变体相比,APE-DE在收敛精度和收敛速度方面均展现出了显著的优势。此外,为了评估APE-DE在解决现实问题中的有效性,将所提算法应用于光伏模型的参数识别问题。
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.
作者
翟雪玉
杨卫中
ZHAI Xueyu;YANG Weizhong(College of Information and Electrical Engineering,China Agriculture University,Beijing 100083,China;Key Laboratory of Agricultural Machinery Monitoring and Big Data Application,Ministry of Agriculture and Rural Affairs,Beijing 100083,China)
出处
《计算机科学》
北大核心
2025年第S1期629-642,共14页
Computer Science
基金
国家重点研发计划项目(2021YFB3901302)。
关键词
差分进化算法
参数自适应
自引导扰动补偿
极性维度交互
多样性增强
Differential evolution
Parameter adaptation
Self-guided disturbance compensation
Extreme dimension exchange
Diversity enhancement
