基于动态种群多策略差分进化模型的多目标进化算法

doi:10.3969/j.issn.0372-2112.2016.06.031

电子学报 ›› 2016, Vol. 44 ›› Issue (6): 1472-1480.DOI: 10.3969/j.issn.0372-2112.2016.06.031

基于动态种群多策略差分进化模型的多目标进化算法

王亚辉¹, 吴金妹¹, 贾晨辉²

1. 华北水利水电大学机械学院, 河南郑州 450011;
2. 河南科技大学机电工程学院, 河南洛阳 471023

收稿日期:2015-05-11 修回日期:2015-09-20 出版日期:2016-06-25
- 作者简介:
- 王亚辉 女,1970年出生,河南濮阳人,副教授、硕士生导师,主要从事先进制造技术和现代设计方法研究工作.E-mail:wangyahui@ncwu.edu.cn;吴金妹 女,1976年生,海南屯昌人,讲师,主要从事机械设计制造及设备方面的研究工作.E-mail:wujinmei@ncwu.edu.cn;贾晨辉 男,1970年11月出生,博士、副教授、硕士研究生导师.主要研究方向为:计算机集成制造技术、系统工程、虚拟样机设计技术、机器人技术的研究工作.
- 基金资助:
- 国家自然科学基金 (No.51475142)

Multi-objective Evolutionary Algorithm Based on Dynamic Population Multi-strategy Differential Models

WANG Ya-hui¹, WU Jin-mei¹, JIA Chen-hui²

1. School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450011, China;
2. School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, Henan 471023, China

Received:2015-05-11 Revised:2015-09-20 Online:2016-06-25 Published:2016-06-25
- Supported by:
- National Natural Science Foundation of China (No.51475142)

摘要/Abstract

摘要：

针对复杂的多目标优化问题,根据不同差分进化策略的特点,提出一种基于动态种群多策略差分进化模型和分解机制的多目标进化算法(MOEA/D-DPMD).该算法将种群划分为3个子种群,每个子种群分配一种差分进化策略.为了提高算法的性能,依据每种差分进化策略的贡献度,动态的调整子种群的规模,各差分进化策略之间相互配合协同进化.采用具有复杂的PS的LZ09系列基准函数,测试新算法的性能,仿真结果表明邻域规模为25时性能最好.通过不同差分进化策略之间的对比分析,新算法也具有较强的优势.将其与MOEAD/DE和NSGA-II算法对比分析,结果显示该算法的收敛性和多样性均优于另外两种算法,是求解复杂多目标问题的有效方法.

关键词: 分解机制, 多策略差分进化, 动态种群, 多目标优化

Abstract:

According to the characteristics of differential evolution, a multi-objective evolutionary algorithm based on dynamic population multi-strategy differential models and decomposition (MOEA/D-DPMD) is proposed to solve the expensive problems.The algorithm divides the population into three sub-populations and each sub-population is corresponding to a differential evolution strategy.In order to improve the performance of the algorithm, the size of sub-population is adjusted dynamically on the basis of a differential evolution strategy contribution.Each strategy is adopted to participate in coordination during the evolution process.Through the test simulation on the LZ09 benchmarks with complicated Pareto Set (PS), MOEA/D-DPMD shows a best performance with a neighborhood size of 25.Via the comparative analysis of different schemes of differential strategy, MOEA/D-DPMD also performs well.The experimental results indicate that MOEA/D-DPMD has a better performance in terms of convergence and diversity compared with MOEA/D and NSGA-II, which is an effective way for solving complex multi-objective optimization problems.

Key words: decomposition mechanism, multi-strategy differential evolution, dynamic population, multi-objective optimization

中图分类号:

TP18

王亚辉, 吴金妹, 贾晨辉. 基于动态种群多策略差分进化模型的多目标进化算法[J]. 电子学报, 2016, 44(6): 1472-1480.

WANG Ya-hui, WU Jin-mei, JIA Chen-hui . Multi-objective Evolutionary Algorithm Based on Dynamic Population Multi-strategy Differential Models[J]. Acta Electronica Sinica, 2016, 44(6): 1472-1480.

参考文献

[1] Deb K,Pratap A,Agarwal S,et al.A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[2] Zitzler E,Laumanns M,Thiele L.SPEA2:Improving the strength Pareto evolutionary algorithm for multi-objective optimization[A].Proceedings of the Evolutionary Methods for Design,Optimization and Control[C].Athens:International Center for Numerical Methods in Engineering,2002.95-100.
[3] Knowles J D,Corne D W.Approximating the non-dominated front using the Pareto archive evolution strategy[J].Evolutionary Computation,2000,8(2):149-172.
[4] Zhang Q,Li H.MOEA/D:A multi-objective evolutionary algorithm based on decomposition[J].IEEE Transactions on Evolutionary Computation,2007,11(6):712-731.
[5] Zitzler E,Kunzli S.Indicator-based selection in multiobjective search[J].Proc of the Parallel Problem Solving from Nature (PPSN VⅢ)[C].Berlin,Heidelberg:Springer-Verlag,2004.LNCS 3242,832-842.
[6] Bader J,Zitzler E.HypE:An algorithm for fast hypervolume-based many-objective optimization[J].Evolutionary Computation,2011,19(1):45-76.
[7] Hernadez-Diaz AG,Santana-Quintero LV,Coello Coello CA,Molina J.Pareto-Adaptive ε-dominance[J].Evolutionary Computation,2007,15(4):493-517.
[8] Li H,Zhang Q.Multi-objective optimization problems with complicated Pareto sets,MOEA/D and NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation,2009,13(2):284-302.
[9] 周爱民,张青富,张桂戌.一种基于混合高斯模型的多目标进化算法[J].软件学报,2014,25(5):913-928. Zhou Ai-min,ZHANG Qing-fu,ZHANG Gui-xu.Multi-objective evolutionary algorithm based on mixture Gaussian models[J].Journal of Software,2014,25(5):913-928.(in Chinese)
[10] H Li,D Landa-Silva.An adaptive evolutionary multi-objective approach based on simulated annealing[J].Evolutionary Computation,2011,19(4):561-595.
[11] N Moubayed,A Petrovski,J McCall.A novel smart multi-objective particle swarm optimization based on decomposition[A].International Conference on Parallel Problem Solving from Nature[C].Berlin Heidelberg:Springer,2010.1-10.
[12] Z Martinez,C Coello Coello.A multi-objective particle swarm optimizer based on decomposition[A].Genetic and Evolutionary Computation Conference[C].New York:ACM,2011.69-76.
[13] 王亚辉,贾晨辉,赵仁鹏.基于分解机制的多目标蝙蝠算法[J].农业机械学报,2015,46(4):316-324. Wang Yahui,Jia Chenhui,Zhao RenPeng.Multi-objective bat algorithm based on decomposition[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(4):316-324.(in Chinese)
[14] Q Zhang,H Li,D Maringer,E Tsang.MOEA/D with NBI-style Tchebycheff approach for portfolio management[A].IEEE Congress on Evolutionary Computation[C].Barcelona:IEEE,2010.1-8.
[15] H Ishibuchi,Y Sakane,N Tsukamoto,Y Nojima.Adaptation of scalarizing functions in MOEA/D:An adaptive scalar zing function-based multi-objective evolutionary algorithm[A].International Conference on Evolutionary Multi-Criterion Optimization[C].Berlin Heidelberg:Springer,2009.438-452.
[16] H Ishibuchi,Y Sakane,N Tsukamoto,Y Nojima.Simultaneous use of different scalarizing functions in MOEA/D[A].Genetic and Evolutionary Computation Conference[C].New York:ACM,2010.519-526.
[17] Y Tan,Y Jiao,H Li,X Wang.MOEA/D+uniform design:a new version of MOEA/D for optimization problems with many objectives[J].Compute Opera Res,2013,40(6):1648-1660.
[18] X Ma,Y Qi,L Li,F Liu,et al.MOEA/D with uniform decomposition measurement for many-objective problems[J].Soft Compute,2014,18:2541-2564.
[19] F Gu,H Liu.A novel weight design in multi-objective evolutionary algorithm[A].International Conference on Computational Intelligence and Security[C].Nanning:IEEE,2010.137-141.
[20] Y Qi,X Ma,F Liu,L Jiao,et al.MOEA/D with adaptive weight adjustment[J].Evolutionary Computation,2014,22(2):231-264.
[21] C Chen,Q Zhang.Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization[A].IEEE Congress on Evolutionary Computation[C].Trondheim:IEEE,2009.209-216.
[22] W Huang,H Li.On the differential evolution schemes in MOEA/D[A].International Conference on Natural Computation[C].Yantai:IEEE,2010.2788-2792.
[23] H Li,D Landa-Silva.An adaptive evolutionary multi-objective approach based on simulated annealing[J].Evolutionary Computation,2011,19(4):561-595.
[24] 毕晓君,肖婧.基于自适应差分进化的多目标进化算法[J].计算机集成制造系统,2011,17(12):2660-2665. Bi Xiao-jun,Xiao Jing.Multi-objective evolutionary algorithm based on self-adaptive differential evolution[J].Computer Integrated Manufacturing Systems,2011,17(12):2660-2665.(in Chinese)
[25] Miettinen K.Nonlinear Multi-objective Optimization.Norwell[M].MA:Kluwer,1999.
[26] Das I,J E Dennis.Normal-boundary intersection:A new method for generating Pareto optimal points in multi-criteria optimization problems[J].SIAM J Optim,1998,8(3):631-657.
[27] Messac A,Ismail-Yahaya,C Mattson.The normalized constraint method for generating the Pareto frontier[J].Struct Multidisc Optim,2003,25:86-98.
[28] 詹腾,张屹,朱大林,刘铮,等.基于多策略差分进化的元胞多目标遗传算法[J].计算机集成制造系统,2014,20(6):1342-1351. Zhan Teng,Zhang Yi,Zhu Da-lin,Liu Zheng,et al.A cellular genetic algorithm based on multi-strategy differential evolution for multi-objective optimization[J].Computer Integrated Manufacturing Systems,2014,20(6):1342-1351.(in Chinese)
[29] Zitzler E,Thiele L.Multi-objective evolutionary algorithms:a comparative case study and the strength Pareto approach[J].IEEE Transactions on Evolutionary Computation,1999,3(4):257-271.
[30] Bosman T,Thierens D.The balance between proximity and diversity in multi-objective evolutionary algorithms[J].IEEE Transactions on Evolutionary Computation,2003,7(2):174-188.
[31] Ishibuchi H,Akedo N,Nojima Y.Relation between neighborhood size and MOEA/D performance on many-objective problems[A].Evolutionary Multi-Criterion Optimization[C].Berlin Heidelberg:Springer,2013.459-474.
[32] DEB K,AGRAWAL R B.Simulated binary crossover for continuous search space[J].Complex Systems,1995,9(2):115-148.

基于动态种群多策略差分进化模型的多目标进化算法

Multi-objective Evolutionary Algorithm Based on Dynamic Population Multi-strategy Differential Models

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	钟沛龙, 黎明, 何超, 陈昊. 基于SOM聚类和自适应算子选择的高维多目标进化算法[J]. 电子学报, 2022, 50(8): 1959-1974.
[2]	魏振春, 傅宇, 马仲军, 吕增威, 石雷, 张本宏. 带时间窗的无线可充电传感器网络多目标路径规划算法[J]. 电子学报, 2022, 50(8): 1819-1829.
[3]	汪禹宏, 张屹. 基于适应度指导交配限制策略的重组算子与多目标优化研究[J]. 电子学报, 2022, 50(3): 710-717.
[4]	张屹, 陆逸舟, 王帅, 陆曈曈. 基于多源交配选择策略的重组算子与多目标优化研究[J]. 电子学报, 2021, 49(9): 1754-1760.
[5]	王蔚龙, 李勇军, 赵尚弘, 辛宁, 赵海燕, 张泰江. 基于两阶段帕累托优化的卫星下行链路功率分配方法[J]. 电子学报, 2021, 49(6): 1101-1107.
[6]	王亚良, 倪晨迪, 金寿松. 基于多邻居结构的自适应元胞差分算法[J]. 电子学报, 2021, 49(3): 578-585.
[7]	罗智勇, 朱梓豪, 谢志强, 孙广路. 面向云计算的花朵差分授粉工作流多目标优化算法研究[J]. 电子学报, 2021, 49(3): 470-476.
[8]	张磊, 刘庆, 杨尚尚, 杨海鹏, 程凡, 马海平. 基于双编码的重叠社团检测多目标优化方法[J]. 电子学报, 2021, 49(11): 2101-2107.
[9]	崔玉亚, 张德干, 张婷, 杨鹏, 朱浩丽. 一种面向移动边缘计算的多用户细粒度任务卸载调度方法[J]. 电子学报, 2021, 49(11): 2202-2207.
[10]	刘冰洁, 毕晓君. 一种基于角度信息的约束高维多目标进化算法[J]. 电子学报, 2021, 49(11): 2208-2216.
[11]	王海东, 刘云敬, 康琳, 武迎春. 射频能量捕获传感网移动能量源的布置策略研究[J]. 电子学报, 2020, 48(12): 2367-2375.
[12]	黎明, 黄珊, 陈昊, 李军华. 基于类圆映射的高维多目标可视化方法[J]. 电子学报, 2019, 47(6): 1185-1193.
[13]	谢承旺, 张飞龙, 陆建波, 肖驰, 龙广林. 一种多策略协同的多目标萤火虫算法[J]. 电子学报, 2019, 47(11): 2359-2367.
[14]	张磊, 毕晓君, 王艳娇. 基于重新匹配策略的ε约束多目标分解优化算法[J]. 电子学报, 2018, 46(5): 1032-1040.
[15]	韩博文, 姚佩阳, 孙昱. 基于多目标MSQPSO算法的UAVS协同任务分配[J]. 电子学报, 2017, 45(8): 1856-1863.