Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems

Adel M. El-Refaey

doi:doi:10.11648/j.ajnna.20170303.11

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Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems

Adel M. El-Refaey

Published in American Journal of Neural Networks and Applications (Volume 3, Issue 3)

Received: 24 October 2017 Accepted: 20 November 2017 Published: 14 December 2017

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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.

Published in	American Journal of Neural Networks and Applications (Volume 3, Issue 3)
DOI	10.11648/j.ajnna.20170303.11
Page(s)	29-35
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Artificial Immune System, Local Search, Multiobjective Programming, Clonal Selection, Design Optimization

References

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[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.
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[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.
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[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.
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[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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Adel M. El-Refaey. (2017). Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems. American Journal of Neural Networks and Applications, 3(3), 29-35. https://doi.org/10.11648/j.ajnna.20170303.11

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ACS Style

Adel M. El-Refaey. Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems. Am. J. Neural Netw. Appl. 2017, 3(3), 29-35. doi: 10.11648/j.ajnna.20170303.11

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AMA Style

Adel M. El-Refaey. Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems. Am J Neural Netw Appl. 2017;3(3):29-35. doi: 10.11648/j.ajnna.20170303.11

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@article{10.11648/j.ajnna.20170303.11,
  author = {Adel M. El-Refaey},
  title = {Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems},
  journal = {American Journal of Neural Networks and Applications},
  volume = {3},
  number = {3},
  pages = {29-35},
  doi = {10.11648/j.ajnna.20170303.11},
  url = {https://doi.org/10.11648/j.ajnna.20170303.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170303.11},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems
AU  - Adel M. El-Refaey
Y1  - 2017/12/14
PY  - 2017
N1  - https://doi.org/10.11648/j.ajnna.20170303.11
DO  - 10.11648/j.ajnna.20170303.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 29
EP  - 35
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20170303.11
AB  - 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.
VL  - 3
IS  - 3
ER  -

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Author Information

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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Adel M. El-Refaey. (2017). Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems. American Journal of Neural Networks and Applications, 3(3), 29-35. https://doi.org/10.11648/j.ajnna.20170303.11

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ACS Style

Adel M. El-Refaey. Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems. Am. J. Neural Netw. Appl. 2017, 3(3), 29-35. doi: 10.11648/j.ajnna.20170303.11

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AMA Style

Adel M. El-Refaey. Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems. Am J Neural Netw Appl. 2017;3(3):29-35. doi: 10.11648/j.ajnna.20170303.11

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@article{10.11648/j.ajnna.20170303.11,
  author = {Adel M. El-Refaey},
  title = {Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems},
  journal = {American Journal of Neural Networks and Applications},
  volume = {3},
  number = {3},
  pages = {29-35},
  doi = {10.11648/j.ajnna.20170303.11},
  url = {https://doi.org/10.11648/j.ajnna.20170303.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170303.11},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems
AU  - Adel M. El-Refaey
Y1  - 2017/12/14
PY  - 2017
N1  - https://doi.org/10.11648/j.ajnna.20170303.11
DO  - 10.11648/j.ajnna.20170303.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 29
EP  - 35
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20170303.11
AB  - 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.
VL  - 3
IS  - 3
ER  -

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