Share This Article:

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

Abstract Full-Text HTML Download Download as PDF (Size:834KB) PP. 1054-1061
DOI: 10.4236/eng.2011.310131    3,366 Downloads   6,040 Views   Citations

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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

A. Khazaee and H. Naimi, "Two Multi-Objective Genetic Algorithms for Finding Optimum Design of an I-beam," Engineering, Vol. 3 No. 10, 2011, pp. 1054-1061. doi: 10.4236/eng.2011.310131.

References

[1] J. Andersson, “A Survey of Multi-objective Optimization in Engineering Design,” Technical Report No. LiTH-IKP- R-1097, Department of Mechanical Engineering, Linkoping University, Linkoping, 2000.
[2] A. Osyczka, “Multicriteria Optimization for Engineering Design,” In: J. Gero, Ed., Design Optimization, 1985, pp. 193-227.
[3] J. Arkat, L. Hosseini and M. H. Farahani, “Minimization of Exceptional Elements and Voids in the Cell Formation Problem Using a Multi-Objective Genetic Algorithm,” Expert Systems with Applications, Vol. 38, 2011, pp. 9597-9602. doi:10.1016/j.eswa.2011.01.161
[4] B. Forouraghi, “A Genetic Algorithm for Multi-Objective Robust Design,” Journal of Applied Intelligence, Vol. 12, No. 3, 2000, pp. 151-161. doi:10.1023/A:1008356321921
[5] X. Li, “Better Spread and Convergence: Particle Swarm Multi-Objective Optimization Using the Maximum Fitness Function,” Lecture Notes in Computer Science, Vol. 3102, 2004, pp. 117-128. doi:10.1007/978-3-540-24854-5_11
[6] P. R. McMullen, “An Ant Colony Optimization Approach to Addressing a JIT Sequencing Problem with Multiple Objectives,” Artificial Intelli-gence in Engineering, Vol. 15, No. 3, 2001, pp. 309-317. doi:10.1016/S0954-1810(01)00004-8
[7] H. Pohlheim, “Ge-netic and Evolutionary Algorithms: Principles, Methods and Algorithms,” 2005. http://www.geatbx.com/docu/index.html
[8] P. G. Espejo, S. Ventura and F. Herrera, “A Survey on the Application of Genetic Programming to Classification,” IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 40, No. 2, 2010, pp. 121-144. doi:10.1109/TSMCC.2009.2033566
[9] H. Gong, R. Zulk-ernine and P. Abolmaesumi, , “A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection,” Proceedings of the 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/ SAWN’05), Canada K7L 3N6, 2005.
[10] E. Ochlak and B. Forouraghi, “A Particle Swarm Algorithm for Multi-Objective Design Optimization,” Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (IC-TAI’06), Philadelphia, 20-23 August 2006.
[11] L. A. Zadeh, “Optimality and Nonscalar-Valued Perfor- mance Criteria,” IEEE Transactions on Automatic Control, Vol. AC-8, 1963, p. 1.
[12] A. Guigue, M. Ahmadi, R. Langlois and J. Hayes, “Pareto Optimality and Multiobjective Trajectory Planning for a 7-DOF Redundant Manipulator,” IEEE Transactions on Ro-botics, Vol. 26, No. 6, 2010, pp. 491-500. doi:10.1109/TRO.2010.2068650
[13] N. O. Da Cunha and E. Polak, “Constrained Minimization under Vector-Valued Crite-ria in Finite Dimensional Spaces,” Journal of Mathematical Analysis and Applications, Vol. 19, 1967, pp. 103-124. doi:10.1016/0022-247X(67)90025-X
[14] 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.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.