DOI QR코드

DOI QR Code

An efficient multi-objective cuckoo search algorithm for design optimization

  • Kaveh, A. (Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology) ;
  • Bakhshpoori, T. (Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology)
  • 투고 : 2015.08.12
  • 심사 : 2015.09.09
  • 발행 : 2016.01.25

초록

This paper adopts and investigates the non-dominated sorting approach for extending the single-objective Cuckoo Search (CS) into a multi-objective framework. The proposed approach uses an archive composed of primary and secondary population to select and keep the non-dominated solutions at each generation instead of pairwise analogy used in the original Multi-objective Cuckoo Search (MOCS). Our simulations show that such a low computational complexity approach can enrich CS to incorporate multi-objective needs instead of considering multiple eggs for cuckoos used in the original MOCS. The proposed MOCS is tested on a set of multi-objective optimization problems and two well-studied engineering design optimization problems. Compared to MOCS and some other available multi-objective algorithms such as NSGA-II, our approach is found to be competitive while benefiting simplicity. Moreover, the proposed approach is simpler and is capable of finding a wide spread of solutions with good coverage and convergence to true Pareto optimal fronts.

키워드

참고문헌

  1. Branke, J., Kaubler, T. and Schmeck, H. (2001), "Guidance in evolutionary multi-objective optimization", Adv. Eng. Softw., 32(6), 499-507. https://doi.org/10.1016/S0965-9978(00)00110-1
  2. Coello, C.A.C., Pulido, G.T. and Lechuga M.S. (2014), "Handling multiple objectives with particle swarm optimization", IEEE Trans. Evol. Comput., 8(3), 256-279.
  3. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002), "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Trans. Evol. Comput., 6(2), 182-197. https://doi.org/10.1109/4235.996017
  4. Gong, W., Cai, Z. and Zhu, L. (2009), "An efficient multiobjective differential evolution algorithm for engineering design", Struct. Multidiscip. Optim., 38(2), 137-157. https://doi.org/10.1007/s00158-008-0269-9
  5. Hanoun, S., Creighton, D. and Nahavandi, S. (2014), "A hybrid cuckoo search and variable neighborhood descent for single and multiobjective scheduling problems", Int. J. Adv. Manuf. Tech., 75(9-12), 1501- 1516. https://doi.org/10.1007/s00170-014-6262-0
  6. Kaveh, A. (2014), Advances in mataheuristic algorithms for optimal design of structures, Springer, Switzerland.
  7. Kaveh, A. and Bakhshpoori, T. (2013), "Optimum design of steel frames using cuckoo search algorithm with Lévy flights", Struct. Des. Tall Spec. Build., 22(13), 1023-1036. https://doi.org/10.1002/tal.754
  8. Kaveh, A., Bakhshpoori, T. and Barkhori, M. (2014), "Optimum design of multi-span composite box girder bridges using Cuckoo Search algorithm", Steel Compos. Struct., 17(5), 705-719. https://doi.org/10.12989/scs.2014.17.5.705
  9. Kanagaraj, G., Ponnambalam, S.G. and Jawahar, N. (2013), "A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems", Comput. Indust. Eng., 66(4), 1115-1124. https://doi.org/10.1016/j.cie.2013.08.003
  10. Ray, T. and Liew, K.M. (2002), "A swarm metaphor for multiobjective design optimization", Eng. Optim., 34(2), 141-153. https://doi.org/10.1080/03052150210915
  11. Shayanfar, M.A., Ashoory, M., Bakhshpoori, T. and Farhadi, B. (2013), "Optimization of modal load pattern for pushover analysis of building structures", Struct. Eng. Mech., 47(1), 119-129. https://doi.org/10.12989/sem.2013.47.1.119
  12. Srivastav, A. and Agrawal, S. (2015), "Multi objective cuckoo search optimization for fast moving inventory items", Adv. Intell. Syst. Comput., 320, 503-510. https://doi.org/10.1007/978-3-319-11218-3_45
  13. Srinivas, N. and Deb, K. (1994), "Multiobjective function optimization using nondominated sorting genetic algorithms", Evol. Comput., 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221
  14. Yahya, M. and Saka, M.P. (2014), "Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights", Autom Construct., 38, 14-29. https://doi.org/10.1016/j.autcon.2013.11.001
  15. Yang, X.S. and Deb, S. (2013), "Multiobjective cuckoo search for design optimization", Comput. Oper. Res., 40(6), 1616-1624. https://doi.org/10.1016/j.cor.2011.09.026
  16. Yang, X.S. and Deb, S. (2010), "Engineering optimisation by cuckoo search", Int. J. Math. Model Numer. Optim., 1(4), 330-343.
  17. Zeltni, K. and Meshoul, S. (2014), "Multi-objective cuckoo Search with leader selection strategies", Combinatorial optimization, Lecture Notes in Computer Science, 8596, 421-432.

피인용 문헌

  1. A comparative study of multi-objective evolutionary metaheuristics for lattice girder design optimization vol.77, pp.3, 2016, https://doi.org/10.12989/sem.2021.77.3.417