Korean Journal of Computational Design and Engineering (한국CDE학회논문집)

Society for Computational Design and Engineering (한국CDE학회)
권호 표지
DOI QR코드

DOI QR Code

Surrogate-Based Improvement on Cuckoo Search for Global Constrained Optimization

근사 최적화를 활용한 뻐꾸기 탐색법의 성능 개선

  • Lee, Se Jung (Department of Mechanical and Information Engineering, The University of Seoul)
  • 이세정 (서울시립대학교 기계정보공학과)
  • Received : 2014.04.19
  • Accepted : 2014.06.03
  • Published : 2014.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

Engineering applications of global optimization techniques are recently abundant in the literature and it may be caused by both new methodologies arising and faster computers coming out. Many of the optimization techniques are based on natural or biological phenomena. This study put focus on enhancing the performace of Cuckoo Search (CS) among them since it has the least number of parameters to tune. The proposed enhancement can be achieved by applying surrogate-based optimization at every cycle of CS, which fortifies the exploitation capability of the original method. The enhanced algorithm has been applied several engineering design problems with constraints. The proposed method shows comparable or superior performance to the original method.

Keywords

References

  1. Beheshti, Z. and Shamsuddin, S.M.H., 2013, A Review of Population-based Meta-Heuristic Algorithm, Int. J. Advances in Soft Computing and Its Appications, 5(1), pp.1-35.
  2. Yang, X.-S., 2010, Nature-Inspired Metaheuristic Algorithms, 2nd Edition, Luniver Press.
  3. Yang, X.-S. and Deb, S., 2009, Cuckoo Search via Levy Flights, in: Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), December 2009, India, IEEE Publications, USA, pp. 210-214.
  4. Yang, X.-S. and Deb, S., 2010, Engineering optimization by cuckoo search, Int. J. Mathematical Modelling and Numerical Optimisation, 1(4), pp.330-343. https://doi.org/10.1504/IJMMNO.2010.035430
  5. Fister, I., Jr, Yang, X.-S., Fister, D. and Fister, I., 2014, Cuckoo Search: A Brief Literature Review. In Cuckoo Search and Firefly Algorithm, Springer, pp.49-62.
  6. Rajabioun, R., 2011, Cuckoo Optimization Algorithm, Applied Soft Computing Journal, 11(8), pp.5508-5518. https://doi.org/10.1016/j.asoc.2011.05.008
  7. Yang, X.-S. and Deb, S., 2013, Cuckoo Search: Recent Advances and Applications, Neural Computing and Applications, 24(1), pp.169-174.
  8. Bhargava, V., Fateen, S.E.K. and Bonilla-Petriciolet, A., 2013, Cuckoo Search: A New Natureinspired Optimization Method for Phase Equilibrium Calculations, Fluid Phase Equilibria, 337, pp.191-200. https://doi.org/10.1016/j.fluid.2012.09.018
  9. Bulatovic, R.R., Dordevic, S.R. and Dordevic, V.S., 2013, Cuckoo Search Algorithm: A Metaheuristic Approach to Solving the Problem of Optimum Synthesis of a Six-bar Double Dwell Linkage, Mechanism and Machine Theory, 61(C), pp.1-13. https://doi.org/10.1016/j.mechmachtheory.2012.10.010
  10. Burnwal, S. and Deb, S., 2012, Scheduling Optimization of Flexible Manufacturing System Using Cuckoo Search-based Approach, The International Journal of Advanced Manufacturing Technology, 64(5-8), pp.951-959.
  11. Panda, R., Agrawal, S. and Bhuyan, S., 2013, Edge Magnitude Based Multilevel Thresholding Using Cuckoo Search Technique, Expert Systems With Applications, 40(18), pp.7617-7628. https://doi.org/10.1016/j.eswa.2013.07.060
  12. Yang, X.-S. and Deb, S., 2014, Cuckoo Search: Recent Advances and Applications, Neural Computing & Applications, 24, pp.169-174. https://doi.org/10.1007/s00521-013-1367-1
  13. Lee, S.J., 2012, An Efficient Heuristic Algorithm of Surrogate-Based Optimization for Global Optimal Design Problems, Transactions of the Society of CAD/CAM Engineers, 17(5), pp.375-386. https://doi.org/10.7315/CADCAM.2012.375
  14. Wang, G.G. and Shan, S., 2007, Review of Metamodeling Techniques in Support of Engineering Design Optimization, Journal of Mechanical Design, 129, pp.370-380. https://doi.org/10.1115/1.2429697
  15. Kazemi, M., Wang, G., Rahnamayan, S. and Gupta, K., 2011, Metamodel-Based Optimization for Problems with Expensive Objective and Constraint Functions, Journal of Mechanical Design, 133(1), pp.1-7.
  16. Akay, B. and Karaboga, D., 2010, Artificial Bee Colony Algorithm for Large-scale Problems and Engineering Design Optimization, Journal of Intelligent Manufacturing, 23(4), pp.1001-1014.
  17. Bui, T., Pham, H. and Hasegawa, H., 2013, Improve Self-Adaptive Control Parameters in Differential Evolution for Solving Constrained Engineering Optimization Problems, Journal of Computational Science and Technology, 7(1), pp.59-74. https://doi.org/10.1299/jcst.7.59
  18. Rao, R.V., Savsani, V.J. and Vakharia, D.P., 2011, Teaching-learning-based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems, Computer-Aided Design, 43(3), pp.303-315. https://doi.org/10.1016/j.cad.2010.12.015
  19. Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M., 2013, Mine Blast Algorithm: A New Population Based Algorithm for Solving Constrained Engineering Optimization Problems, Applied Soft Computing Journal, 13(5), pp.2592-2612. https://doi.org/10.1016/j.asoc.2012.11.026
  20. Viana, F.A.C., 2011, SURROGATES Toolbox User's Guide, Version 3.0, available at http://sites.google.com/site/felipeacviana/surrogatestoolbox.
  21. Yang, X.-S., 2013, Cuckoo Search (CS) Algorithm, available at http://www.mathworks.com/matlabcentral/fileexchange/29809-cuckoo-search-cs-algorithm.