A Research on Entropy of Information Compression Operator-based Multi-stage Genetic Algorithm

2010 ◽  
Author(s):  
Wen-jie Lu ◽  
Lin-rong Pang ◽  
Hui-xin Yu ◽  
Rui-jiang Wang
Keyword(s):  
Genetic Algorithm ◽  
Information Compression ◽  
Multi Stage

Structure of Multi-Stage Composite Genetic Algorithm (MSC-GA) and its performance

2011 ◽  
Vol 38 (7) ◽  
pp. 8929-8937 ◽  
Author(s):  
Fachao Li ◽  
Li Da Xu ◽  
Chenxia Jin ◽  
Hong Wang
Keyword(s):  
Genetic Algorithm ◽  
Multi Stage

scholarly journals Multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using Genetic Algorithm

2012 ◽  
Vol 55 (4) ◽  
pp. 229-236 ◽  
Author(s):  
Yosuke KITAGAWA ◽  
Koki KITAGAWA ◽  
Masaki NAKAMIYA ◽  
Masahiro KANAZAKI ◽  
Toru SHIMADA
Keyword(s):  
Genetic Algorithm ◽  
Conceptual Design ◽  
Hybrid Rocket ◽  
Multi Stage

Process Yield Improvement Through Optimum Design of Fixture Layouts in 3D Multi-Station Assembly Systems

2007 ◽  
Author(s):  
T. Phoomboplab ◽  
D. Ceglarek
Keyword(s):  
Genetic Algorithm ◽  
Design Space ◽  
Industrial Case Study ◽  
Process Yield ◽  
Space Reduction ◽  
Multi Stage ◽  
Complex Design ◽  
Hammersley Sequence ◽  
System Robustness ◽  
Fixture System

This paper presents a new approach to improve process yield by determining an optimum set of fixture layouts for a given multi-station assembly system which can satisfy: (i) parts and subassemblies locating stability in each fixture layout; and (ii) fixture system robustness against environmental noises in order to minimize product dimensional variability. Three major challenges of the multi-stage assembly processes are addressed: (i) high-dimensional design space; (ii) large and complex design space of each locator; and (iii) the nonlinear relations between locator positions, also called Key Control Characteristics, and Key Product Characteristics. The proposed methodology conducts two-step optimization based on the integration of Genetic Algorithm and Hammersley Sequence Sampling. First, Genetic Algorithm is used for design space reduction by determining the areas of optimal fixture locations in initial design spaces. Then, Hammersley Sequence Sampling uniformly samples the candidate sets of fixture layouts from the areas predetermined by GA for the optimum. The process yield and part instability index are design objectives in evaluating candidate sets of fixture layouts. An industrial case study illustrates and validates the proposed methodology.

Sign in / Sign up

Export Citation Format

Share Document