Adaptive SAGA based on mutative scale chaos optimization strategy

被引：0

作者：

Gao, HC ^{[1
]}

Feng, BQ ^{[1
]}

Zhu, L ^{[1
]}

机构：

[1] Xian Jiaotong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China

来源：

PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年

关键词：

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：

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.

引用

页码：517 / 520

页数：4