On Increasing Computational Efficiency of Evolutionary Algorithms Applied to Large Optimization Problems

被引:0
作者
Glowacki, Maciej [1 ]
Orkisz, Janusz [1 ]
机构
[1] Cracow Univ Technol, Inst Computat Civil Engn, Krakow, Poland
来源
2015 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2015年
关键词
evolutionary algorithms; computation efficiency increase; large non-linear constrained optimization problems;
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
This paper presents new advances in development of dedicated Evolutionary Algorithms (EA) for large non-linear constrained optimization problems. The primary objective of our research is a significant increase of the computational efficiency of the standard EA. The EA are understood here as Genetic Algorithms using decimal chromosomes, three standard operators: selection, crossover, and mutation, as well as additional new speed-up techniques. So far we have preliminarily proposed several general concepts, including smoothing and balancing, a'posteriori solution error analysis and related techniques, as well as an adaptive step-by-step mesh refinement. We discuss here the efficiency of chosen speed-up techniques using simple but demanding benchmark problems, including residual stress analysis in elastic-perfectly plastic bodies under cyclic loadings, and physically based smoothing of experimental data. Particularly, we consider a smoothing technique using average solution curvature, new criteria for selection based on global solution error, as well as a step-by-step mesh refinement combined with smoothing. Preliminary numerical results clearly indicate a possibility of significant acceleration of calculations, as well as practical application of the improved EA to the optimization problems considered.
引用
收藏
页码:2639 / 2646
页数:8
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