An efficient algorithm for complex problems

Author

Li, Bing ; Jiang, Weisun

Author_Institution

Tangshan Univ., China

Volume

1

fYear

1997

fDate

28-31 Oct 1997

Firstpage

703

Abstract

This paper presents a new stochastic optimization algorithm based on a simulated annealing algorithm (SAA), genetic algorithm (GA), and chemotaxis algorithm (CA) which is called SAGACIA. It can be used to solve some complicated optimization problems. SAGACIA integrates some advantages of SAA, GA and CA. It can not only easily escape from local minima, but also converge quickly. Good solutions can be obtained in a short time. SAGACIA has been applied to solve some practical problems, such as scheduling problems, training artificial neural networks, and so on. In all the test cases, the performance of SAGACIA is better than SAA, GA and CA

Keywords

convergence; genetic algorithms; learning (artificial intelligence); neural nets; problem solving; scheduling; simulated annealing; stochastic programming; SAGACIA; artificial neural networks; chemotaxis algorithm; complex problems; convergence; genetic algorithm; local minima; performance; scheduling problems; simulated annealing; stochastic optimization algorithm; training; Artificial neural networks; Cost function; Electrical engineering; Genetic algorithms; Genetic mutations; Processor scheduling; Simulated annealing; Stochastic processes; Temperature; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.672877

Filename

672877