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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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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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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.
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 © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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