Multi-objective optimization of laminated ceramic composites using genetic algorithms

A. D. Belegundu; D. V. Murthy; R. R. Salagame; E. W. Constans

doi:10.2514/6.1994-4363

Multi-objective optimization of laminated ceramic composites using genetic algorithms

A. D. Belegundu, D. V. Murthy, R. R. Salagame, E. W. Constans

Mechanical Engineering

Research output: Contribution to conference › Paper › peer-review

28 Scopus citations

Abstract

In recent years, much interest has been generated for using genetic algorithms to optimize certain classes of problems. Genetic algorithms have definite advantages over some other types of optimization algorithms in that they are quite robust, and do not require knowledge of the gradients of the objective function. Because of their unique breeding and selection processes, genetic algorithms can be used with equal ease on linear and nonlinear optimization problems, which makes them an excellent all-purpose optimization algorithm. This paper will discuss a new genetic algorithm (GENMO) that can be used to simultaneously optimize multiple objectives. Specifically, the GENMO algorithm can be used to generate Pareto sets for two or more conflicting objective functions.

Original language	English (US)
Pages	1015-1022
Number of pages	8
DOIs	https://doi.org/10.2514/6.1994-4363
State	Published - 1994
Event	5th Symposium on Multidisciplinary Analysis and Optimization, 1994 - Panama City Beach, United States Duration: Sep 7 1994 → Sep 9 1994

Other

Other	5th Symposium on Multidisciplinary Analysis and Optimization, 1994
Country/Territory	United States
City	Panama City Beach
Period	9/7/94 → 9/9/94

All Science Journal Classification (ASJC) codes

Mechanical Engineering
Aerospace Engineering

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10.2514/6.1994-4363

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title = "Multi-objective optimization of laminated ceramic composites using genetic algorithms",

abstract = "In recent years, much interest has been generated for using genetic algorithms to optimize certain classes of problems. Genetic algorithms have definite advantages over some other types of optimization algorithms in that they are quite robust, and do not require knowledge of the gradients of the objective function. Because of their unique breeding and selection processes, genetic algorithms can be used with equal ease on linear and nonlinear optimization problems, which makes them an excellent all-purpose optimization algorithm. This paper will discuss a new genetic algorithm (GENMO) that can be used to simultaneously optimize multiple objectives. Specifically, the GENMO algorithm can be used to generate Pareto sets for two or more conflicting objective functions.",

author = "Belegundu, {A. D.} and Murthy, {D. V.} and Salagame, {R. R.} and Constans, {E. W.}",

note = "Publisher Copyright: {\textcopyright} 1994 by Belegundu Published by the AIAA, Inc.; 5th Symposium on Multidisciplinary Analysis and Optimization, 1994 ; Conference date: 07-09-1994 Through 09-09-1994",

year = "1994",

doi = "10.2514/6.1994-4363",

language = "English (US)",

pages = "1015--1022",

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TY - CONF

T1 - Multi-objective optimization of laminated ceramic composites using genetic algorithms

AU - Belegundu, A. D.

AU - Murthy, D. V.

AU - Salagame, R. R.

AU - Constans, E. W.

PY - 1994

Y1 - 1994

N2 - In recent years, much interest has been generated for using genetic algorithms to optimize certain classes of problems. Genetic algorithms have definite advantages over some other types of optimization algorithms in that they are quite robust, and do not require knowledge of the gradients of the objective function. Because of their unique breeding and selection processes, genetic algorithms can be used with equal ease on linear and nonlinear optimization problems, which makes them an excellent all-purpose optimization algorithm. This paper will discuss a new genetic algorithm (GENMO) that can be used to simultaneously optimize multiple objectives. Specifically, the GENMO algorithm can be used to generate Pareto sets for two or more conflicting objective functions.

AB - In recent years, much interest has been generated for using genetic algorithms to optimize certain classes of problems. Genetic algorithms have definite advantages over some other types of optimization algorithms in that they are quite robust, and do not require knowledge of the gradients of the objective function. Because of their unique breeding and selection processes, genetic algorithms can be used with equal ease on linear and nonlinear optimization problems, which makes them an excellent all-purpose optimization algorithm. This paper will discuss a new genetic algorithm (GENMO) that can be used to simultaneously optimize multiple objectives. Specifically, the GENMO algorithm can be used to generate Pareto sets for two or more conflicting objective functions.

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DO - 10.2514/6.1994-4363

M3 - Paper

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EP - 1022

T2 - 5th Symposium on Multidisciplinary Analysis and Optimization, 1994

Y2 - 7 September 1994 through 9 September 1994

ER -

Multi-objective optimization of laminated ceramic composites using genetic algorithms

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