Statistical analysis of a multi-objective optimization algorithm based on a model of particles with vorticity behavior

被引：26
作者：
Meza, Joaquin [1 ]
Espitia, Helbert [1 ]
Montenegro, Carlos [1 ]
Gonzalez Crespo, Ruben [2 ]
机构：
[1] Univ Distrital Francisco Jose de Caldas, Carrera 8 40-62, Bogota, Colombia
[2] Univ Int La Rioja UNIR, Rectorado Via Rey Juan Carlos 1,41, Logrono, La Rioja, Spain
来源：
SOFT COMPUTING | 2016年 / 20卷 / 09期
关键词：
Multi-objective optimization; Particle swarm; Vorticity; EVOLUTIONARY ALGORITHM; PSO;
D O I：
10.1007/s00500-015-1972-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a strategy for multi-objective optimization based upon the behavior of a particle swarm with rotational and linear motion is presented. The strategy for multi-objective optimization is based upon the emulation of the linear and circular movements of a swarm (flock). Thus emerges the physical basis for the cognitive model, which in conjunction with exploration-exploitation results in the proposal of a cognitive algorithm, which is tested through several multi-objective optimization functions. The algorithm proposed is compared with standard particle swarm optimization multi-objective via statistical analysis.
引用
收藏
页码：3521 / 3536
页数：16
相关论文
共 40 条
  • [1] Abido MA, 2008, 2008 12TH INTERNATIONAL MIDDLE EAST POWER SYSTEM CONFERENCE, VOLS 1 AND 2, P485
  • [2] [Anonymous], ESTADISTICA BASICA C
  • [3] [Anonymous], 2006, Int J Comput Intell Res, DOI DOI 10.5019/J.IJCIR.2006.68
  • [4] [Anonymous], 2007, EVOLUTIONARY ALGORIT
  • [5] [Anonymous], 1987, Multiple comparison procedures
  • [6] Bhagavatula SS, 2014, IEEE INT ADV COMPUT, P1206, DOI 10.1109/IAdCC.2014.6779499
  • [7] Cagnina L., 2010, THESIS
  • [8] Cagnina L, 2005, J COMPUT SCI TECHNOL, V5, P204
  • [9] Self-propelled particles with soft-core interactions: Patterns, stability, and collapse
    D'Orsogna, MR
    Chuang, YL
    Bertozzi, AL
    Chayes, LS
    [J]. PHYSICAL REVIEW LETTERS, 2006, 96 (10) : 1 - 4
  • [10] Demsar J, 2006, J MACH LEARN RES, V7, P1
1234