Adaptive SAGA based on mutative scale chaos optimization strategy

Author

Gao, Haichang ; Feng, BoQin ; Zhu, Li

Author_Institution

Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ.

Volume

1

fYear

2005

Firstpage

517

Lastpage

520

Abstract

A hybrid adaptive SAGA based on mutative scale chaos optimization strategy (CASAGA) is proposed to solve the slow convergence, incident getting into local optimum characteristics of the standard genetic algorithm (SGA). The algorithm combined the parallel searching structure of genetic algorithm (GA) with the probabilistic jumping property of simulated annealing (SA), also used adaptive crossover and mutation operators. The mutative scale chaos optimization strategy was used to accelerate the optimum seeking. By comparing the CASAGA with SGA and MSCGA on effectiveness, the CASAGA has more strong searching ability than other two, it can abandon the local optimal solution and find the global one more quickly

Keywords

adaptive systems; chaos; genetic algorithms; simulated annealing; hybrid adaptive SAGA; mutative scale chaos optimization strategy; simulated annealing; standard genetic algorithm; Acceleration; Chaos; Convergence; Engines; Evolution (biology); Genetic algorithms; Genetic mutations; Nonlinear systems; Simulated annealing; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Brain, 2005. ICNN&B '05. International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-9422-4

Type

conf

DOI

10.1109/ICNNB.2005.1614666

Filename

1614666