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.
|Correspondence should be addressed to: email@example.com.|
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.
|View this paper at Google Scholar | DPI Digital Library|
|XML View||PDF Download|
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.
|372||275 downloads||142 downloads|
|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|
 K. Deb, “An efficient constraint handling method for genetic algorithms”, Computer Methods in Applied Mechanics and Engineering, Vol.186, Issue. 2, pp.311, 2000.
 C.A.C. Coello, “Self adaptive penalties for GA based optimization”, In the Proceeding of the congress on Evolutionary Computation, D C Washington, pp.573-580, 1999.
 C.A.C. Coello, E.M. Montes, “Constraint- handling in genetic algorithms through the use of dominance-based tournament selection”, Advanced Enggineering Informatics, Vol. 16, Issue.3, pp.193-197, 2002.
 Z. Guo, S. Wang, X. Yue, D. Jiang, K. Li, “Elite opposition-based artificial bee colony algorithm for global optimization”, International Journal of Engineering Transaction C: Aspects, Vol.28, Issue.9, pp.1268-1275, 2015.
 A. Alfi, A. Khosravi, “Constrained nonlinear optimal control via a hybrid ba-sd”, International Journal of Engineering, Vol.25, Issue.3, pp.197-204, 2012.
 B. Akay, D. Karaboga, “Artificial bee colony algorithm for large scale problems and engineering design optimization”, Journal of Intelligent Manufacturing, Vol.23, Issue.4, pp.1001-1014, 2012.
 H. Garg, “Solving structural engineering design optimization problems using an artificial bee colony algorithm”, Journal of Industrial and Management Optimization, Vol.10, Issue.3, pp.777-794, 2014.
 H. Garg, “A hybrid PSO-GA algorithm for constrained optimization problems”, Applied Mathematics and Computation, Vol.274, Issue.C, pp.292-305, 2016.
 A. Kaveh, S. Talatahari, “An improved ant colony optimization for constrained engineering design problems”, Engineering Computer, Vol.27, Issue.1, pp.155-182, 2010.
 A. Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm”, Computers and Structures, Vol.169, Issue.1, pp.1-12, 2016.
 K. Hans Raj, R.S. Sharma, G.S. Mishra, A. Dua, C. Patvardhan, “An Evolutionary Computational Technique for constrained optimization in engineering design”, Journal of Institutions of Engineers (India), Vol.86, Issue.2, pp.121-128, 2005.
 S. Yang, F. Liu, L. Jiao, “The quantum evolutionary strategies”, Acta Electron Sinica, Vol.29, Issue.12, pp.1873-1877, 2001.
 K.H. Han, J.H. Kim, “Quantum-inspired evolutionary algorithms with a new termination criterion H gate and two-phase scheme”, IEEE Transactions on Evolutionary Computation, Vol.8, Issue.2, pp.156-169, 2004.
 L.D.S. Coelho, “Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems”, Expert systems with Applications, Vol.37, Issue.2, pp.1676-1683, 2010.
 Y. Wang, J. Zhou, L. Mo, S. Ouyang, Y. Zhang, “A clonal real-coded quantum-inspired evolutionary algorithm with cauchy mutation for short-term hydrothermal generation scheduling”, Electrical Power and Energy Systems, Vol. 43, Issue.1, pp.1228-1240, 2012.
 K. Hans Raj, R. Setia, “Quantum seeded evolutionary computational technique for constrained optimization in engineering design and manufacturing”, Struct. Multidisc. Optimization, Vol.55, Issue.3, pp. 751-766, 2017.
 M. Lundy, A. Mees, “Convergence of an annealing algorithm”, Mathematical Programming, Vol.34, Issue.1, pp.111-124, 1986.
 S.E. Moumen, R. Ellaia, R. Aboulaich, “A new hybrid method for solving global optimization problem”, Applied Mathematics and Computation, Vol.218, Issue.7, pp.3265-3276, 2011.
 L. Idoumghar, N. Chérin, P. Siarry, R. Roche, A. Miraoui, “Hybrid ICA-PSO algorithm for continuous optimization”, Applied Mathematics and Computation, Vol.219, Issue.24, pp.11149-11170, 2013.
 P. Civicioglu, “Backtracking search optimization algorithm for numerical optimization problems”, Applied Mathematics Computation, Vol.219, Issue.15, pp.8121-8144, 2013.
 M. Gang, Z. Wei, C. Xiaolin, “A novel particle swarm optimization algorithm based on particle migration”, Applied Mathematics and Computation, Vol. 218, Issue.11, pp.6620-6626, 2012.
 G. Xu, “An adaptive parameter tuning of particle swarm optimization algorithm”, Applied Mathematics and Computation, Vol.219, Issue.9, pp.4560-4569, 2013.
 Y. Jiang, T. Hu, C.C. Huang, X. Wu, “An improved particle swarm optimization algorithm”, Applied Mathematics and Computation, Vol. 193, Issue.1, pp.231-239, 2007.
 M. Xi, J. Sun, W. Xu, “An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position”, Applied Mathematics and Computation, Vol.205, Issue.2, pp.751-759, 2008.
 L.Y. Chuang, S.W. Tsai, C.H. Yang, “Chaotic catfish particle swarm optimization for solving global numerical optimization problems”, Applied Mathematics and Computation, Vol.217, Issue.16, pp.6900-6916, 2011.
 J. Li, Y. Tang, C. Hua, X. Guan, “An improved krill herd algorithm: Krill herd with linear decreasing step”, Applied Mathematics and Computation, Vol.234, Issue.C, pp.356-367, 2014.
 B.H.F. Hasan, I.A. Doush, E.A. Maghayreh, F. Alkhateeb, M. Hamdan, “Hybridizing Harmony Search algorithm with different mutation operators for continuous problems”, Applied Mathematics and Computation, Vol.23, Issue.2, pp.1166-1182, 2014.
 F. Zhoug, H. Li, S. Zhoug, “A modified ABC algorithm based on improved-global-best-guided approach and adaptive limit strategy for global optimization”, Applied Soft Computing, Vol.46, Issue.C, pp.469-486, 2016.
 X.S. Yang, “Test problems in optimization”, in Engineering Optimization: An Introduction with metaheuristic applications (John Wiley and Sons), USA, pp.1-23, 2010.
 J. Arora, “Introduction to optimum design, MGraw-Hill, New York, pp.1-728, 1989.
 S. He, E. Prempain, Q.H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems”, Engineering Optimization, Vol.36, Issue.5, pp.585-605, 2004.
 L.C. Cagnina, S.C. Esquivel, C.A.C. Coello, “Solving engineering optimization problems with the simple constrained particle swarm optimizer”, Informatica, Vol.32, Issue.3, pp.319-326, 2008.
 A.D. Belegundu, “A study of mathematical programming methods for structural optimization”, Internal Report Department of Civil and Environmental Engineering (University of lowa), USA, pp.1-26, 1982.
 T. Ray, P. Saini, “Engineering design optimization using a swarm with intelligent information sharing among individuals”, Engineering Optimization, Vol.33, Issue.33, pp.735-748, 2001.
 Q. He, L. Wang, “An effective co-evolutionary particle swarm optimization for constrained engineering design problems”, Engineering Application with Artificial intelligence, Vol.20, Issue.1, pp.89-99, 2007.
 Y. Wang, Z.X. Cai, Y.R. Zhou, “Accelerating adaptive trade-off model using shrinking space technique for constrained evolutionary optimization”, International Journal for Numerical Methods in Engineering, Vol.77, Issue.11, pp.1501-1534, 2009.
 H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi, “Water cycle algorithm- a noval metaheuristic optimaization method for solving constrained engineering optimization problems”, Computers and Structures, Vol.110, Issue.11, pp.151-166, 2012.
 W. Long, X. Liang, Y. Huang, Y. Chen, 2013. “A hybrid differential evolution augmented lagrangian method for constrained numerical and engineering optimization”, Computer-Aided Design, Vol.45, Issue.12, pp.1562-1574, 2013.
 E. Sandgren, “Nonlinear integer and discrete programming in mechanical design”, In the proceedings of the 1988 ASME design technology conference, USA, pp.95-105, 1988.
 C. Zhang, H.P. Wang, “Mixed-discrete nonlinear optimization with simulated annealing”, Engineering Optimization, Vol.17, Issue.3, pp.263-280, 1993.
 K. Deb, A.S. Gene, “A robust optimal design technique for mechanical component design”, In the proceeding of the D. Dasrupta and Z. Michalewicz (Eds.) Evolutionary algorithms in engineering applications, Berlin, pp. 497-514, 1997.
 A. Gandomi, A.H., Yang, X.S., A. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems”, Engineering with Computers, Vol.29, Issue.1, pp.17-35, 2013.
 S.E. He, E. Prempain, Q.H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems”, Engineering Optimization, Vol.36, Issue.5, pp.585-605, 2004.
 K.S. Lee, Z.W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice”, Computer Methods in Applied Mechanics and Engineering, Vol.194, Issue.36-38, pp.3902-3933, 2005.
 E.M. Montes, C.A.C. Coello, J.V. Reyes, L.M. Davila, “Multiple trial vectors in differential evolution for engineering design”, Engineering Optimization, Vol.39, Issue.5, pp.567-589, 2007
 E.M. Montes, C.A.C. Coello, “An empirical study about the usefulness of evolution strategies to solve constrained optimization problems”, International Journal of General Systems, Vol. 37, Issue.4, 443-473, 2008.
 A. Kaveh, S. Talatahari,. “Engineering optimization with hybrid particle swarm and ant colony optimization”, Asian journal of civil engineering (building and housing), Vol.10, Issue.6, pp.611-628, 2009.
 A.O. Youyun, C. Hongqin, “An adaptive differential evolution algorithm to solve constrained optimization problems in engineering design”, Engineering, Vol.2, Issue.1, pp.65-77, 2010.
 S.S. Rao, “Engineering optimization”, John Wiley and Sons, Greece, pp.56-82,1996.
 H.L. Li, P. Papalambros, “A production system for use of global optimization knowledge”, ASME Jounal of Mechanism Transmission and Automation in Design, Vol. 107, Issue.2, pp.277-284, 1985.
 J.K. Kunag, S.S. Rao, L. Chen, “Taguchi-aided search method for design optimization of engineering systems”, Engineering Optimization, Vol. 30, Issue.1, pp.1-23, 1998.