Evolutionary programming

Author

Cao, Y.J. ; Wu, Q.H.

Author_Institution

Dept. of Electr. Eng. & Electron., Liverpool Univ., UK

fYear

1997

fDate

13-16 Apr 1997

Firstpage

443

Lastpage

446

Abstract

This paper presents a mixed-variable evolutionary programming (MVEP) for solving mechanical design optimization problems which contain integer, discrete, zero-one and continuous variables. The MVEP provides an improvement in global search and convergence performance in a mixed-variable space. An approach to handle various kinds of variables and constraints is discussed. Two examples of mechanical design optimization are tested, which demonstrate that the proposed approach is superior to current methods for finding optimum solution, both in the quality of solution and convergence performance

Keywords

CAD; convergence; genetic algorithms; mechanical engineering; mechanical engineering computing; problem solving; search problems; constraints; continuous variables; convergence performance; discrete variables; global search; integer variables; mechanical design optimization; mixed-variable evolutionary programming; problem solving; quality; zero-one variables; Constraint optimization; Design optimization; Gears; Genetic programming; Linear programming; Matrix converters; Quadratic programming; Symmetric matrices; Teeth; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1997., IEEE International Conference on

Conference_Location

Indianapolis, IN

Print_ISBN

0-7803-3949-5

Type

conf

DOI

10.1109/ICEC.1997.592352

Filename

592352