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Improved Genetic Algorithm and Particle Swarm Optimization as well as Comparison between Them(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2002年02期
Page:
14-20
Research Field:
Publishing date:

Info

Title:
Improved Genetic Algorithm and Particle Swarm Optimization as well as Comparison between Them
Author(s):
Ren Bin;  Feng Zhenping
Institute of Turbomachinery, Xian Jiaotong University,710049,Xian,PRC
Keywords:
function optimization;  improved genetic algorithm;  par ticle swarm optimization
PACS:
TP18
DOI:
-
Abstract:
As a new kind of optimization search techniques, the evolut ionary algorithms are widely used to solv e differ ent pr oblems in optimal areas. After a careful research, t he simple genetic algor ithm has been impr oved by adopting float- coding method, simulated annealing algor ithm and sor ted stochastic fitness selection strategy; and has been applied to mathematic function optimization. In addition, a new evolutionar y algorit hm- Particle Sw arm Optimization is intro duced and applied to the same mathematic function opt imization. The optimization r esults ar e compared with each other in this paper. The compar at ive result indicates that the Improved Genetic Algor ithm and Particle Sw arm Optimizatio n are both robust. But Par ticle Swarm Opt imization can obtain the optimum solut ions more easily than the Improved Genetic Algor ithm, and it is a good optimization method w ith strong competitiveness.

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Last Update: 2013-04-29