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F. de Toro, J. Or-tega, J. Fernández and A. Díaz, “PSFGA: A Parallel Genetic Algorithm for Multi-Objective Optimization,” Proceedings of the 10th Euromicro Workshop on Parallel, Distributed and Network-Based Processing (EUROMICRO-PDP.02), Canary Islands, 9-11 January 2002.

has been cited by the following article:

• TITLE: Two Multi-Objective Genetic Algorithms for Finding Optimum Design of an I-beam

  AUTHORS: Ali Khazaee, Hossein Miar Naimi

  KEYWORDS: Genetic Algorithm, Multi-Objective, I-Beam, Optimization

  JOURNAL NAME: Engineering, Vol.3 No.10, October 21, 2011

  ABSTRACT: Many engineering design problems are characterized by presence of several conflicting objectives. This requires efficient search of the feasible design region for optimal solutions which simultaneously satisfy multiple design objectives. Genetic algorithm optimization (GAO) is a powerful search technique with faster convergence rates than traditional evolutionary algorithms. This paper applies two GAO-based approaches to multi-objective engineering design and finds design variables through the feasible space. To demonstrate the utility of the proposed methods, the multi-objective design of an I-beam will be presented.