基于双档案种群大小自适应方法的改进差分进化算法  

Improved differential evolution algorithm based on dual-archive population size adaptive method

作　　者：黄亚伟 钱雪忠 宋威 HUANG Yawei;QIAN Xuezhong;SONG Wei(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi Jiangsu 214122,China)

机构地区：[1]江南大学人工智能与计算机学院,江苏无锡214122

出　　处：《计算机应用》2024年第12期3844-3853,共10页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62076110)。

摘　　要：针对现有差分进化(DE)算法在处理种群多样性降低和局部最优问题时,种群大小改进方法的性能不足,提出一种基于双档案种群大小自适应方法(APSA)的差分进化算法(APDE)。首先,构建2个档案分别用于记录在先前进化中丢弃的个体和实验个体;其次,根据种群分布状态变化衡量多样性变化,并在多样性下降时从档案中选择个体加入种群,从而提升种群的多样性并增强跳出局部最优的能力;最后,基于APSA方法,提出一种改进的DE算法——APDE。在CEC2017测试集和兰纳-琼斯势问题上的广泛测试结果表明,APDE算法在30个测试函数上的基于Friedman test的平均排名中优于其他5种DE算法,并在至少20%的测试函数上取得了显著提升;同时,APDE算法在解决势能最小化上也取得了最佳性能。Addressing the poor performance of population size improvement methods in the existing Differential Evolution(DE)algorithms when dealing with decreased population diversity and local optimum challenges,a dual-Archive Population Size adaptive Differential Evolution algorithm(APDE)was proposed on the basis of dual-Archive Population Size Adaptive method(APSA).Firstly,two archives were constructed to record individuals that had been discarded in previous evolutions and experimental individuals respectively.Then,diversity changes were measured according to the variations in the population distribution state.And when population diversity decreased,the individuals from the archives were selected and added to the population to enhance the population diversity and the ability to escape from the local optimum.Finally,an improved DE algorithm based on APSA method,APDE,was proposed.Results of extensive tests on CEC2017 test set and Lennard-Jones potential problem show that APDE algorithm outperforms five other DE algorithms in the average ranking based on Friedman test on 30 benchmark functions,and significant improvements are obtained on at least 20%of these functions.At the same time,APDE algorithm also achieves the best performance in solving the minimization of potential energy.

关 键 词：差分进化算法 双档案 多样性度量 自适应种群大小 数值优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]