A particle swarm and chaos combination approach for vehicle simulator optimization

被引：0
|
作者：
Zhao, Qiang [1 ]
Gao, Fang [2 ]
机构：
[1] NE Forestry Univ, Sch Traff Transportat Engn, Harbin 150040, Peoples R China
[2] Harblin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
来源：
ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 2 | 2006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：
Mechanism design is one crucial step in vehicle simulator developing. A combined approach of particle swarm optimization (PSO) algorithm and mutative scale chaos search algorithm is proposed for the parameters optimization of motion mechanism of vehicle simulator. The PSO algorithm is run to get the global best particle as the candidate solution, and then mutative scale chaos search iterations are employed to improve the local search ability. This combined search approach can solve the invalidation problem of chaos optimization algorithm in a large scale searching, meanwhile it can improve local search precision of PSO. This approach is successfully used in design of a vehicle simulator.
引用
收藏
页码：986 / +
页数：2
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