July  2014, 10(3): 777-794. doi: 10.3934/jimo.2014.10.777

Solving structural engineering design optimization problems using an artificial bee colony algorithm

1. 

School of Mathematics and Computer Applications, Thapar University Patiala, Patiala - 147004, Punjab, India

Received  June 2012 Revised  June 2013 Published  November 2013

The main goal of the present paper is to solve structural engineering design optimization problems with nonlinear resource constraints. Real world problems in engineering domain are generally large scale or nonlinear or constrained optimization problems. Since heuristic methods are powerful than the traditional numerical methods, as they don't requires the derivatives of the functions and provides near to the global solution. Hence, in this article, a penalty guided artificial bee colony (ABC) algorithm is presented to search the optimal solution of the problem in the feasible region of the entire search space. Numerical results of the structural design optimization problems are reported and compared. As shown, the solutions by the proposed approach are all superior to those best solutions by typical approaches in the literature. Also we can say, our results indicate that the proposed approach may yield better solutions to engineering problems than those obtained using current algorithms.
Citation: Harish Garg. Solving structural engineering design optimization problems using an artificial bee colony algorithm. Journal of Industrial & Management Optimization, 2014, 10 (3) : 777-794. doi: 10.3934/jimo.2014.10.777
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show all references

References:
[1]

Journal of Intelligent Manufacturing, 23 (2012), 1001-1014. doi: 10.1007/s10845-010-0393-4.  Google Scholar

[2]

McGraw-Hill, New York, 1989. Google Scholar

[3]

PhD thesis, Department of Civil and Environmental Engineering, University of Iowa, Iowa, USA, 1982. Google Scholar

[4]

Informatica, 32 (2008), 319-326. Google Scholar

[5]

Expert Systems with Applications, 37 (2010), 1676-1683. doi: 10.1016/j.eswa.2009.06.044.  Google Scholar

[6]

Engineering Optimization, 32 (2000), 275-308. doi: 10.1080/03052150008941301.  Google Scholar

[7]

Computers in Industry, 41 (2000), 113-127. doi: 10.1016/S0166-3615(99)00046-9.  Google Scholar

[8]

Advanced Engineering Informatics, 16 (2002), 193-203. doi: 10.1016/S1474-0346(02)00011-3.  Google Scholar

[9]

AIAA Journal, 29 (1991), 2013-2015. Google Scholar

[10]

Computer Methods in Applied Mechanics and Engineering, 186 (2000), 311-338. doi: 10.1016/S0045-7825(99)00389-8.  Google Scholar

[11]

(Eds. D. Dasgupta, Z. Michalewicz,), Evolutionary Algorithms in Engineering Applications, Springer, Berlin, (1997), 497-514. doi: 10.1007/978-3-662-03423-1_27.  Google Scholar

[12]

Computer Science and Informatics, 26 (1986), 30-45. Google Scholar

[13]

Computer Methods in Applied Mechanics and Engineering, 196 (2007), 803-817. doi: 10.1016/j.cma.2006.06.010.  Google Scholar

[14]

Computer Methods in Applied Mechanics and Engineering, 197 (2008), 3080-3091. doi: 10.1016/j.cma.2008.02.006.  Google Scholar

[15]

A. H. Gandomi, X. S. Yang, and A. H. Alavi, Mixed variable structural optimization using firefly algorithm,, Computers & Structures, 89 (): 2325.   Google Scholar

[16]

Engineering with Computers, 29 (2003), 17-35. doi: 10.1007/s00366-011-0241-y.  Google Scholar

[17]

Engineering Applications of Artificial Intelligence, 20 (2007), 89-99. doi: 10.1016/j.engappai.2006.03.003.  Google Scholar

[18]

Engineering Optimization, 36 (2004), 585-605. doi: 10.1080/03052150410001704854.  Google Scholar

[19]

Journal of Global Optimization, 35 (2006), 521-549. doi: 10.1007/s10898-005-3693-z.  Google Scholar

[20]

McGraw-Hill, New York, 1972. Google Scholar

[21]

Simulation, 62 (1994), 242-254. doi: 10.1177/003754979406200405.  Google Scholar

[22]

Engineering Optimization, 39 (2007), 679-700. doi: 10.1080/03052150701252664.  Google Scholar

[23]

Proceedings of the 2003 IEEE Swarm Intelligence Symposium, (2003), 53-57. Google Scholar

[24]

Advanced Engineering Informatics, 20 (2006), 7-21. doi: 10.1016/j.aei.2005.09.001.  Google Scholar

[25]

Transactions of the ASME, Journal of Mechanical Design, 116 (1994), 405-411. doi: 10.1115/1.2919393.  Google Scholar

[26]

Technical report, TR06, Erciyes University, Engineering Faculty, Computer Engineering Department 2005. Google Scholar

[27]

Applied Mathematics and Computation, 214 (2009), 108-132. doi: 10.1016/j.amc.2009.03.090.  Google Scholar

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Artificial Intelligence Review ,(2012), 1-37. doi: 10.1007/s10462-012-9328-0.  Google Scholar

[29]

Applied Soft Computing, 11 (2011), 652-657. doi: 10.1016/j.asoc.2009.12.025.  Google Scholar

[30]

Asian journal of civil engineering (building and housing), 10 (2009), 611-628. Google Scholar

[31]

Engineering Computations, 27 (2010), 155-182. doi: 10.1108/02644401011008577.  Google Scholar

[32]

Computer Methods in Applied Mechanics and Engineering, 194 (2005), 3902-3933. doi: 10.1016/j.cma.2004.09.007.  Google Scholar

[33]

Applied Mathematics and Computation, 188 (2007), 1567-1579. doi: 10.1016/j.amc.2006.11.033.  Google Scholar

[34]

Engineering Optimization, 44 (2012), 537-550. doi: 10.1080/0305215X.2011.598520.  Google Scholar

[35]

Springer - Verlag, Berlin, 1994.  Google Scholar

[36]

International Journal of General Systems, 37 (2008), 443-473. doi: 10.1080/03081070701303470.  Google Scholar

[37]

Engineering Optimization, 39 (2007), 567-589. doi: 10.1080/03052150701364022.  Google Scholar

[38]

Chaos, Solitons & Fractals, 42 (2009), 662-668. Google Scholar

[39]

ASME Journal of Engineering for Industries, 98 (1976), 1021-1025. doi: 10.1115/1.3438995.  Google Scholar

[40]

Journal of the Institution of Engineers India Part Me Mechanical Engineering Division, 86 (2005), 121-128. Google Scholar

[41]

3rd edition, John Wiley & Sons, Chichester, 1996. Google Scholar

[42]

IEEE Transactions on Evolutionary Computation, 7 (2003), 386-396. doi: 10.1109/TEVC.2003.814902.  Google Scholar

[43]

Engineering Optimization, 33 (2001), 735-748. doi: 10.1080/03052150108940941.  Google Scholar

[44]

Proceedings of the ASME Design Technology Conference, F.L. Kissimine, 1988, 95-105. Google Scholar

[45]

IEEE International Conference on Evolutionary Computation, Piscataway, NJ: IEEE Press, (1998), 69-73. Google Scholar

[46]

Engineering Optimization, 37 (2005), 399-409. doi: 10.1080/03052150500066737.  Google Scholar

[47]

Engineering Optimization, 21 (1993), 277-291. doi: 10.1080/03052159308940980.  Google Scholar

[48]

Information Sciences, 178 (2008), 3043-3074. doi: 10.1016/j.ins.2008.02.014.  Google Scholar

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