基于方向向量采样的大规模多目标进化优化
PDF下载 (427)熊英剑,史旭华*.基于方向向量采样的大规模多目标进化优化[J].宁波大学学报(理工版),2024,37(2):1-9.DOI:10.20098/j.cnki.1001-5132.2023.0240
XIONG Yingjian,SHI Xuhua.Large-scale multi-objective evolutionary optimization based on direction vector sampling[J].Journal of Ningbo University(Natural Science & Engineering Edition),2024,37(2):1-9.DOI:10.20098/j.cnki.1001-5132.2023.0240
| Title: | Large-scale multi-objective evolutionary optimization based on direction vector sampling |
| 作者: | 熊英剑, 史旭华* |
| Author(s): | XIONG Yingjian, SHI Xuhua |
| 关键词: | 高维; 大规模多目标; 方向向量; 定向采样 |
| Keywords: | high-dimensional; large-scale multi-objective; direction vector; directional sampling |
| 分类号: | TP301.6 |
| DOI: | 10.20098/j.cnki.1001-5132.2023.0240 |
| 文献标识码: | A |
| 摘要: | 针对在高维决策空间的大规模多目标优化中难以搜索产生有希望的后代个体, 从而导致常规算法性能恶化的问题, 提出了一种基于方向向量采样的大规模多目标进化算法. 该算法首先选取一些接近理想点的优秀候选个体, 然后根据候选个体分阶段创建方向向量, 最后执行定向采样策略(收敛性相关采样策略和多样性相关采样策略)来生成子代个体, 生成的子代个体性能算法收敛加速. 实验结果表明, 与5种具有代表性的大规模多目标进化算法相比, 该算法在多达2000个决策变量的大规模多目标优化测试, 仍具有较强的寻优能力. |
| Abstract: | To tackle the difficulty to search for promising offspring individuals in large-scale multi-objective optimization of high-dimensional decision space, which leads to the deterioration of the performance of conventional algorithms, a large-scale multi-objective evolutionary algorithm based on direction vector sampling is proposed. The algorithm first selects some good candidate individuals close to the ideal point, then creates direction vectors in stages, and executes directional sampling strategies, that is, convergence-related sampling strategy and diversity-related sampling strategy to generate offspring individuals. In this way, the offspring individuals with better performance are generated to accelerate the convergence of the algorithm. Experimental results show that compared with five representative large-scale multi-objective evolutionary algorithms, the proposed algorithm has retained strong optimizing ability on large-scale multi-objective optimization test problems with as many as 2 000 decision variables |
| 参考文献 /References: | [1].孙刚, 伍江江, 陈浩, 等. 一种基于切比雪夫距离的隐式偏好多目标进化算法[J]. 计算机科学, 2022, 49(6):297-304. [2].谢承旺, 余伟伟, 郭华, 等. DAV-MOEA: 一种采用动态角度向量支配关系的高维多目标进化算法[J]. 计算机学报, 2022, 45(2):317-333. [3].顾清华, 骆家乐, 李学现. 基于小生境的多目标进化算法[J]. 计算机工程与应用, 2023, 59(1):126-139. [4].PENG C D, LIU H L, GOODMAN E D, et al. A two-phase framework of locating the reference point for decomposition-based constrained multi-objective evolutionary algorithms[J]. Knowledge-Based Systems, 2022, 239:107933. [5].GAN X J, SUN J, GONG D W, et al. An adaptive reference vector-based interval multiobjective evolutionary algorithm[J]. IEEE Transactions on Evolutionary Com- putation, 2023, 27(5):1235-1249. [6].PARSONS L, HAQUE E, LIU H. Subspace clustering for high dimensional data[J]. ACM SIGKDD Explorations Newsletter, 2004, 6(1):90-105. [7].ANTONIO L M, COELLO C A C. Use of cooperative coevolution for solving large scale multiobjective optimization problems[C]//2013 IEEE Congress on Evolutionary Computation. Cancun, Mexico. IEEE, 2013: 2758-2765. [8].KUKKONEN S, LAMPINEN J. GDE3: the third evolution step of generalized differential evolution [C]//2005 IEEE Congress on Evolutionary Computation. Edinburgh, UK. IEEE, 2005:443-450. [9].MIGUEL A L, COELLO C A. Decomposition-based approach for solving large scale multi-objective problems [C]//Handl J, Hart E, Lewis P, et al. International conference on parallel problem solving from nature. Cham: Springer, 2016:525-534. [10].ZHANG Q F, LI H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731. [11].MA X L, LIU F, QI Y T, et al. A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(2):275-298. [12].ZHANG X Y, TIAN Y, CHENG R, et al. A decision variable clustering-based evolutionary algorithm for large- scale many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(1):97-112. [13].ZILLE H, ISHIBUCHI H, MOSTAGHIM S, et al. A framework for large-scale multiobjective optimization based on problem transformation[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(2):260-275. [14].HE C, LI L H, TIAN Y, et al. Accelerating large-scale multiobjective optimization via problem reformulation[J]. IEEE Transactions on Evolutionary Computation, 2019, 23(6):949-961. [15].TIAN Y, ZHENG X T, ZHANG X Y, et al. Efficient large-scale multiobjective optimization based on a competitive swarm optimizer[J]. IEEE Transactions on Cybernetics, 2020, 50(8):3696-3708. [16].HE C, CHENG R, YAZDANI D. Adaptive offspring generation for evolutionary large-scale multiobjective optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(2):786-798. [17].QIN S F, SUN C L, JIN Y C, et al. Large-scale evolutionary multiobjective optimization assisted by directed sampling[J]. IEEE Transactions on Evolutionary Computation, 2021, 25(4):724-738. [18].GIAGKIOZIS I, PURSHOUSE R C, FLEMING P J. An overview of population-based algorithms for multi- objective optimisation[J]. International Journal of Systems Science, 2015, 46(9):1572-1599. [19].CHENG R, JIN Y C, OLHOFER M, et al. A reference vector guided evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5):773-791. [20].YANG X, ZOU J, YANG S X, et al. A fuzzy decision variables framework for large-scale multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation, 2023, 27(3):445-459. [21].DEB K, THIELE L, LAUMANNS M, et al. Scalable test problems for evolutionary multiobjective optimization [M]//Advanced Information and Knowledge Processing. London: Springer-Verlag, 2005:105-145. [22].TIAN Y, CHENG R, ZHANG X Y, et al. PlatEMO: a MATLAB platform for evolutionary multi-objective optimization[educational forum[J]. IEEE Computational Intelligence Magazine, 2017, 12(4):73-87. [23].SHANG K, ISHIBUCHI H, HE L J, et al. A survey on the hypervolume indicator in evolutionary multiobjective optimization[J]. IEEE Transactions on Evolutionary Computation, 2021, 25(1):1-20. |
| 备注/Memo: | 收稿日期: 2023−02−24. 宁波大学学报（理工版）网址: http://journallg.nbu.edu.cn/ 基金项目: 国家自然科学基金（61773225）; 宁波市重点研发计划暨“揭榜挂帅”（2023Z067）. 第一作者: 熊英剑, 硕士研究生, 主要研究方向: 模式识别与智能系统等. E-mail: xyj2879566278@163.com *通信作者: 史旭华, 副教授, 主要研究方向: 模式识别与智能系统等. E-mail: shixuhua@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
