摘要
针对利用遗传算法解决参数维度高、计算复杂,且适应度依赖于其他工具的问题,提出一种加快遗传算法收敛速度的聚集算子.该算子首先利用AP(affinity propagation)聚类对种群进行划分,然后通过主成分分析(PCA)对每个聚簇降维,再利用加权最小二乘法在低维空间下将种群分布拟合成二次曲面,并将计算极值点作为优势个体返回到原始空间.实验结果表明,相比于传统遗传算法,聚集算子在保证优化精度的同时可有效提高收敛速度.
Aiming at solving problems of high parameter dimension,complex calculation and the fitness depended on other tools with GA,we proposed an aggregation operator to accelerate the convergence of genetic algorithm.Firstly,the affinity propagation(AP)clustering was used to divide the populat ion into sub-clusters,and then the dimension of each cluster was reduced by principal component analysis(PCA).Secondly,the population distribution was fitted to quadric surfaces by weighted least squ are method in lower dimensional space.Finally,the calculated extreme points were returned to the original space as the dominant individuals.The experimental results show that compared with the traditional genetic algorithm,aggregation operator can effectively improve the convergence speed and ensure the optimization accuracy.
作者
裴莹
苏山
付加胜
韩霄松
PEI Ying;SU Shan;FU Jiasheng;HAN Xiaosong(College of Information Engineering,Changchun University of Finance and Economics,Changchun 130122,China;Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education,College of Computer Science and Technology,Jilin Unive rsity,Changchun 130012,China;Institute of Drilling Technology,CNPC Engineering Technology R&D Company Limited,Beijing 102206,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2021年第3期602-608,共7页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61972174)
吉林省科技发展计划项目(批准号:20190302107GX)
吉林省产业技术专项研究与开发项目(批准号:2019C053-7)
广东省应用基础研究重点项目(批准号:2018KZDXM076)
广东省重点学科建设计划项目(批准号:2016GDYSZDXK036)。
关键词
复杂问题求解
遗传算法
快速收敛
聚集算子
complex problems solving
genetic algorithm
fast convergence
aggregation operator
