Multi-Objective Compact Differential Evolution

被引:0
|
作者
Osorio Velazquez, Jesus Moises [1 ]
Coello Coello, Carlos A. [1 ]
Arias-Montano, Alfredo [2 ]
机构
[1] CINVESTAV IPN, Av IPN 2508, Mexico City 07360, DF, Mexico
[2] IPN ESIME, Mexico City 07340, DF, Mexico
关键词
ALGORITHMS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A wide range of problems in engineering require the simultaneous optimization of several objectives. Given the nature of such problems, it is often the case that the optimization process needs to take place from a device with very limited resources. Compact algorithms are a suitable alternative for being implemented in devices with limited computing resources, but so far, they have been used only to solve single-objective optimization problems. Here, we present a multi-objective compact algorithm based on differential evolution. The proposed algorithm obtains competitive results (and even better in some cases) than state-of-the-art multi-objective evolutionary algorithms while using less memory resources because of its statistical representation of the population.
引用
收藏
页码:49 / 56
页数:8
相关论文
共 50 条
  • [41] Multi-objective differential evolution - algorithm, convergence analysis, and applications
    Xue, F
    Sanderson, AC
    Graves, RJ
    2005 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-3, PROCEEDINGS, 2005, : 743 - 750
  • [42] Multi-objective differential evolution and its application to enterprise planning
    Xue, F
    Sanderson, AC
    Graves, RJ
    2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-3, PROCEEDINGS, 2003, : 3535 - 3541
  • [43] Multi-Objective Optimal Power Flow Using Differential Evolution
    Abido, M. A.
    Al-Ali, N. A.
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2012, 37 (04) : 991 - 1005
  • [44] An Improved Differential Evolution for Constrained Multi-objective Optimization Problems
    Song, Erping
    Li, Hecheng
    Wanma, Cuo
    2020 16TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2020), 2020, : 269 - 273
  • [45] Data Clustering Using Multi-objective Differential Evolution Algorithms
    Suresh, Kaushik
    Kundu, Debarati
    Ghosh, Sayan
    Das, Swagatam
    Abraham, Ajith
    FUNDAMENTA INFORMATICAE, 2009, 97 (04) : 381 - 403
  • [46] A Multi-Objective Differential Evolution Approach for the Question Selection Problem
    Paul, Dimple V.
    Pawar, Jyoti D.
    2014 FIFTH INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES (ICADIWT), 2014, : 219 - 225
  • [47] Multi-objective strength Pareto chaotic differential evolution algorithm
    Zhang, M. (zmnwpu@126.com), 2012, Northeast University (27):
  • [48] Multi-Objective Diagrid Facade Optimization Using Differential Evolution
    Chatzikonstantinou, Ioannis
    Ekici, Berk
    Sariyildiz, I. Sevil
    Koyunbaba, Basak Kundakci
    2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2015, : 2311 - 2318
  • [49] Multi-Objective Differential Evolution Algorithm for Underwater Image Restoration
    Sanchez-Ferreira, Camilo
    Ayala, Helon V. H.
    Coelho, Leandro dos S.
    Munoz, Daniel
    Farias, Mylene C. Q.
    Llanos, Carlos H.
    2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2015, : 243 - 250
  • [50] Multi-objective particle swarm-differential evolution algorithm
    Yi-xin Su
    Rui Chi
    Neural Computing and Applications, 2017, 28 : 407 - 418