基于行列式点过程的多目标进化问题解的质量评估方法研究  

Study on Quality Assessment Method for Multi Objective Evolutionary Problem Solutions Based on Deterministic Point Process

作　　者：王聪颖 WANG Congying(School of Computer Science and Technology,University of Science and Technology of China,Anhui 230022,China)

机构地区：[1]中国科学技术大学计算机科学与技术学院,安徽230022

出　　处：《电子技术（上海）》2024年第6期47-51,共5页Electronic Technology

摘　　要：阐述在多目标进化算法中,从非支配解中选择更好的解作为后代时,一般考虑收敛性和多样性。通过使用目标向量与理想点之间的L1范数来衡量解的收敛性,用解之间的距离代表多样性,所有解对之间的关系构成一种新的指标矩阵,称为距离收敛值矩阵。此外,引入行列式点过程对子集进行多样化选择,行列式点过程表明,在子集内部的元素之间差异越大,行列式的值就越大。在21个不同的常见测试实例中,将所提出的算法与四种最先进的MOEAs进行比较。实证结果表明,所提出的算法在各种类型的测试实例中具有通用性,并且优于几种最先进的MOEAs。This paper describes that in multi-objective evolutionary algorithms,convergence and diversity are generally considered when selecting the better solution from the nondominant solution as the offspring.It measures the convergence of the solution by using the L1 norm between the target vector and the ideal point,using the distance between the solutions to represent diversity,and the relationship between all solution pairs constitutes a new indicator matrix,called the Distance Convergence ValueMatrix.In addition,it introduces the determinant point procedure to diversify the selection of subsets,which shows that the greater the difference between the elements within the subset,the greater the value of the determinant.It compares the proposed algorithm with four stateof-the-art MOEAs in 21 different common test examples.The empirical results show that the proposed algorithm is universal in various types of test cases and outperforms several state-of-the-art MOEAs.

关 键 词：进化算法 多样性 多目标优化 行列式点过程 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]