A Quantum Inspired Evolutionary Computational Technique with Applications to Structural Engineering Design
|Astuti. V.1 , K. Hansraj2 , A. Srivastava3|
1 Dept. of Mathematics, Dayalbagh Educational Institute, Agra, India.
2 Dept. of Mechanical Engineering, Dayalbagh Educational Institute, Agra, India.
3 Dept. of Mathematics, University of Kiel, Kiel, Germany.
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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-5 , Page no. 20-33, May-2017
Online published on May 30, 2017
Copyright © Astuti. V., K. Hansraj, A. Srivastava . This 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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IEEE Style Citation: Astuti. V., K. Hansraj, A. Srivastava, “A Quantum Inspired Evolutionary Computational Technique with Applications to Structural Engineering Design”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.20-33, 2017.
MLA Style Citation: Astuti. V., K. Hansraj, A. Srivastava "A Quantum Inspired Evolutionary Computational Technique with Applications to Structural Engineering Design." International Journal of Computer Sciences and Engineering 5.5 (2017): 20-33.
APA Style Citation: Astuti. V., K. Hansraj, A. Srivastava, (2017). A Quantum Inspired Evolutionary Computational Technique with Applications to Structural Engineering Design. International Journal of Computer Sciences and Engineering, 5(5), 20-33.
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|A new Quantum Inspired Evolutionary Computational Technique (QIECT) is reported in this work. It is applied to a set of standard test bench problems and a few structural engineering design problems. The algorithm is a hybrid of quantum inspired evolution and real coded Genetic evolutionary simulated annealing strategies. It generates initial parents randomly and improves them using quantum rotation gate. Subsequently, Simulated Annealing (SA) is utilized in Genetic Algorithm (GA) for the selection process for child generation. The convergence of the successive generations is continuous and progresses towards the global optimum. Efficiency and effectiveness of the algorithm are demonstrated by solving a few unconstrained Benchmark Test functions, which are well-known numerical optimization problems. The algorithm is applied on engineering optimization problems like spring design, pressure vessel design and gear train design. The results compare favorably with other state of art algorithms, reported in the literature. The application of proposed heuristic technique in mechanical engineering design is a step towards agility in design.|
|Key-Words / Index Term :|
|Constraint Optimization, Mechanical Engineering Design problems, Quantum Inspired Evolutionary Computational Technique, Unconstrained Optimization|
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