Particle swarm variants: standardized convergence analysis

被引:43
作者
Cleghorn, Christopher W. [1 ]
Engelbrecht, Andries P. [1 ]
机构
[1] Univ Pretoria, Dept Comp Sci, ZA-0002 Pretoria, South Africa
关键词
Particle swarm optimization; Theoretical analysis; Particle convergence; SELECTION; STABILITY;
D O I
10.1007/s11721-015-0109-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an objective function specially designed for the convergence analysis of a number of particle swarm optimization (PSO) variants. It was found that using a specially designed objective function for convergence analysis is both a simple and valid method for performing assumption free convergence analysis. It was also found that the canonical particle swarm's topology did not have an impact on the parameter region needed to ensure convergence. The parameter region needed to ensure convergent particle behavior was empirically obtained for the fully informed PSO, the bare bones PSO, and the standard PSO 2011 algorithm. In the case of the bare bones PSO and the standard PSO 2011, the region needed to ensure convergent particle behavior differs from previous theoretical work. The difference in the obtained regions in the bare bones PSO is a direct result of the previous theoretical work relying on simplifying assumptions, specifically the stagnation assumption. A number of possible causes for the discrepancy in the obtained convergent region for the standard PSO 2011 are given.
引用
收藏
页码:177 / 203
页数:27
相关论文
共 36 条
[1]  
[Anonymous], 2011, TECHNICAL REPORT
[2]  
[Anonymous], 2002, Computational Intelligence an Introduction
[3]   A Study of Collapse in Bare Bones Particle Swarm Optimization [J].
Blackwell, Tim .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2012, 16 (03) :354-372
[4]   SPSO2011-Analysis of Stability, Local Convergence, and Rotation Sensitivity [J].
Bonyadi, Mohammad Reza ;
Michalewicz, Zbigniew .
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2014, :9-15
[5]   Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization [J].
Campana, Emilio F. ;
Fasano, Giovanni ;
Pinto, Antonio .
JOURNAL OF GLOBAL OPTIMIZATION, 2010, 48 (03) :347-397
[6]  
Cleghorn CW, 2014, 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), P2524, DOI 10.1109/CEC.2014.6900439
[7]   A generalized theoretical deterministic particle swarm model [J].
Cleghorn, Christopher W. ;
Engelbrecht, Andries P. .
SWARM INTELLIGENCE, 2014, 8 (01) :35-59
[8]   The particle swarm - Explosion, stability, and convergence in a multidimensional complex space [J].
Clerc, M ;
Kennedy, J .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (01) :58-73
[9]   Roaming Behavior of Unconstrained Particles [J].
Engelbrecht, A. P. .
2013 1ST BRICS COUNTRIES CONGRESS ON COMPUTATIONAL INTELLIGENCE AND 11TH BRAZILIAN CONGRESS ON COMPUTATIONAL INTELLIGENCE (BRICS-CCI & CBIC), 2013, :104-111
[10]   Particle Swarm Optimization: Global Best or Local Best? [J].
Engelbrecht, A. P. .
2013 1ST BRICS COUNTRIES CONGRESS ON COMPUTATIONAL INTELLIGENCE AND 11TH BRAZILIAN CONGRESS ON COMPUTATIONAL INTELLIGENCE (BRICS-CCI & CBIC), 2013, :124-135