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Abstract: . In this paper, we apply an elitist multi-objective genetic algorithm for solving mechanical component design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods in that they can find multiple Pareto-optimal solutions in one single simulation run. The proposed algorithm (we call NSGA-II) is a much improved version of the originally proposed nondominated sorting GA (NSGA) in that... (Update)
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...only a few studies where an MOEA is speci cally designed for handling constraints. Among all methods, the usual penalty function approach [11, 2] where a penalty proportional to the total constraint violation is added to all objective functions. When applying this procedure, all...
.... by Tanaka [12] and OSY used by Osyczka, Kundu [13] The problems chosen from the engineering domains are Two Bar Truss Design used by Deb [14] and Welded Beam design used by Deb [14] All these problems are consmined multi objective problems. Table 1 shows the variable bounds,...
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BibTeX entry: (Update)
Deb, K., Pratap, A., Moitra, S. (2000). Mechanical component design for multiple objectives using elitist non-dominated sorting GA. Proceedings of the Parallel Problem Solving from Nature VI Conference, pp. 859-868. http://citeseer.ist.psu.edu/deb00mechanical.html More
@inproceedings{ deb00mechanical,
author = "K. Deb and A. Pratap and S. Moitra",
title = "Mechanical Component Design for Multiple Objectives Using Elitist Non-Dominated Sorting {GA}",
booktitle = "Parallel Problem Solving from Nature - {PPSN} {VI} 6th International Conference",
month = "16-20",
publisher = "Springer Verlag",
address = "Paris, France",
editor = "Hans-Paul Schwefel {Marc Schoenauer, Kalyanmoy Deb, G{\"u}nter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo}",
year = "2000",
url = "citeseer.ist.psu.edu/deb00mechanical.html" }
Citations (may not include all citations):104 Evolutionary algorithms for multiobjective optimization: Met.. - Zitzler - 1999
59 A fast elitist non-dominated sorting genetic algorithm for m.. - Deb, Agrawal et al. - 2000
49 The Pareto archived evolution strategy: A new baseline algor.. - Knowles, Corne - 1999
39 Simulated binary crossover for continuous search space - Deb, Agrawal - 1995
38 Engineering optimization methods and applications (context) - Reklaitis, Ravindran et al. - 1983
33 Multi-Objective function optimization using non-dominated so.. (context) - Srinivas, Deb - 1995
18 An efficient constraint handling method for genetic algorith.. - Deb
17 Genetic algorithm design of Pareto-optimal broad band microw.. (context) - Weile, Michielssen et al. - 1996
14 Multiobjective dynamic optimization of an industrial Nylon 6.. (context) - Mitra, Deb et al. - 1998
11 An augmented Lagrange multiplier based method for mixed inte.. (context) - Kannan, Kramer - 1995
10 A robust optimization procedure for mechanical component des.. (context) - Deb, Goyal - 1998
1 An interactive multistage -inequality constraint method for.. (context) - Palli, Azram et al. - 1999
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