Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems
American Journal of Neural Networks and Applications
Volume 3, Issue 3, June 2017, Pages: 29-35
Received: Oct. 24, 2017; Accepted: Nov. 20, 2017; Published: Dec. 14, 2017
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Author
Adel M. El-Refaey, Basic and Applied Science Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt
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Abstract
In this paper, an artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS) and local search (LS) is propose to solve Multiobjective engineering design problems. This paper presents an optimal design of a linear synchronous motor (LSM) considering two objective functions namely, maximum force and minimum saturation and then design of air-cored solenoid with maximum inductance and minimum volume as the objective functions. The proposed approach uses Local search, dominance principle and feasibility to identify solutions that deserve to be cloned.
Keywords
Artificial Immune System, Local Search, Multiobjective Programming, Clonal Selection, Design Optimization
To cite this article
Adel M. El-Refaey, Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems, American Journal of Neural Networks and Applications. Vol. 3, No. 3, 2017, pp. 29-35. doi: 10.11648/j.ajnna.20170303.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
A. A. Mousa et al, (2011), “A hybrid ant colony optimization approach based local search scheme for multiobjective design optimizations”, Electric Power Systems Research, vol.81, pp. 1014-1023.
[2]
A. D. Deshpande, J. R. Rinderle, (2001), "Linear Electric Drive for UMM", Technical Report, Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst.
[3]
A. D. Deshpande, (2002), "A Study of Methods to Identify Constraint Dominance In Engineering Design Problems", M. S. Thesis, Mechanical and Industrial Engineering Department, University of Massachusetts, Amhers, California Linear Drives, Inc.
[4]
A. M. Arasomwan and A. O. Adewumi, (2014), “Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems,” The Scientific World Journal, Volume 2014, Article ID 798129, 23 pages.
[5]
C. A. Coello Coello, (2002), “Theoretical and numerical constraint handling techniques used with evolutionary algorithms: A survey of the state of the art,” Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 11/12, pp. 1245–1287.
[6]
C. A. Coello Coello and N. Cruz Cort´es, (2002) "An approach to solve multiobjective optimization problems based on an artificial immune system", in First International Conference on Artificial Immune Systems (ICARIS’2002), J. Timmis and P. J. Bentley (Eds.), University of Kent at Canterbury: UK, Sept. 2002, pp. 212–221. ISBN 1-902671-32-5.
[7]
C. A. Coello Coello, D. A. Van Veldhuizen, and G. B. Lamont, (2002), “Evolutionary algorithms for solving multiobjective problems”, Kluwer Academic Publishers, New York, ISBN 0-3064-6762-3.
[8]
C. A. Coello Coello and N. Cruz Cort´es, (2005), "Solving multiobjective optimization problems using an artificial immune system", Genetic Programming and Evolvable Machines, vol. 6 p 163-190, Springer Sinece + Business Media, Inc.
[9]
J. F. Gieras, Z. J. Pieck, (1999), "Linear Synchronous Motors: Transportation and Automation Systems", CRC Press.
[10]
Kaisa M. Miettinen, (2002), "Nonlinear Multiobjective- Optimization", Kluwer Academic Publishers.
[11]
L. Nunes de Castro and F. J. Von Zuben, (1999), “Artificial immune systems: Part I: Basic theory and applications”, Technical Report TR-DCA 01/99, FEEC/UNICAMP, Brazil.
[12]
L. Nunes de Castro and F. J. Von Zuben, (2001), "aiNet: An artificial immune network for data analysis", Data Mining: A Heuristic Approach, Idea Group Publishing, USA, pp. 231–259.
[13]
L. Nunes de Castro and F. J. Von Zuben, (2002), “Learning and optimization using the clonal selection principle”, IEEE Transactions on Evolutionary Computation, vol. 6, no. 3, pp. 239–251.
[14]
Marco Farina et al, (2001), “Cost-effective Evolutionary Strategies for Pareto Optimal Front Approximation in Multiobjective Shape Design Optimization of Electromagnetic Devices”, Department of Electrical Engineering, UNIVERSITY OF PAVIA, Italy.
[15]
R. Filomeno Coelho, (2004), "Multicriteria Optimization with Expert Rules for Mechanical Design", Dissertation, Université Libre De Bruxelles Faculté Des Sciences Appliquées.
[16]
Rao S. S., (2009), "Engineering Optimization: Theory and Practice", (4rd ed). New York: Wiley.
[17]
W. F. Abd El-Wahed, E. M. Zaki, A. El-Refaey, (2010), "Artificial Immune System based Neural Networks for Solving Multi-objective Programming Problems", Egyptian Information Journal Vol. (11) No. 2 pp. 59-65.
[18]
W. F. Abd El-Wahed, E. M. Zaki, A. El-Refaey, (2010), "Reference Point Based Multi-Objective Optimization Using Hybrid Artificial Immune System", Universal Journal of Computer Science and Engineering Technology, Vol. (1) No. 1 pp: 24-30.
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