A modified firefly algorithm for engineering design optimization problems

Iranian Journal of Science and Technology - Transactions of Mechanical Engineering

Volumn 38, Issue M2, 2014, Pages 403-421

  Kazemzadeh Parsi, M J ^a  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Engineering optimization; Firefly algorithm; Metaheuristic optimization; Sizing optimization; Truss structures

Indexed keywords

EID: 84937764496     PISSN: 22286187     EISSN: 23641835     Source Type: Journal    
DOI: None     Document Type: Article

References (77)

1. Kazemzadeh-Parsi, M. J. & Daneshmand, F. (2013). Inverse geometry heat conduction analysis of functionally graded materials using smoothed fixed grid finite elements. Inverse Problems in Science and Engineering, Vol. 21, No. 2, pp. 235-250.
2. Talbi, E. (2009). Metaheuristics: from design to implementation. Hoboken, New Jersey, USA: John Wiley & Sons.
3. Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. 2nd edition, United Kingdom: Luniver Press.
4. Goldberg, D. (1989). Genetic algorithms in search optimization and machine learning. Addison-Wesley.
5. Back, T., Hoffmeister, F. & Schwefel, H. (1991). A survey of evolution strategies. Proceedings of the Fourth International Conference on Genetic Algorithms and their Applications, 2-9.
6. Fogel, L. (1994). Evolutionary programming in perspective: The top-down view. In: Computational Intelligence: Imitating Life, edited by Zurada, J.M., Marks, Jr., R., and Robinson, C., IEEE Press, Piscataway, NJ, USA.
7. Eberhart, R. C. & Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan.
8. Storn, R. & Price, K. (1995). Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, Berkeley, CA, USA.
9. Dorigo, M., Maniezzo, V. & Colorni, A. (1996). The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B, Vol. 26, No. 1, pp. 29-41.
10. Nakrani, S. & Tovey, C. (2004). On honey bees and dynamic server allocation in Internet hostubg centers. Adaptive Behavior, Vol. 12, pp. 223-240.
11. Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S. & Zaidi, M. (2005). The bees algorithm. Technical Note, Manufacturing Engineering Center, Cardiff University.
12. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report, Erciyes University.
13. Yang, X. S. & Deb, S. (2009). Cuckoo search via Lévy flights. Proceedings of the world congress on nature & biologically inspired computing (NaBIC 2009), IEEE Publications, pp. 210-214.
14. Oftadeh, R., Mahjoob, M. J. & Shariatpanahi, M. (2010). A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search. Comput Math Appl, Vol. 60, pp. 2087-2098.
15. Yang, X. S. (2010b). A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization NISCO edited by Gonzalez, J.R. et al., Studies In Computational Intelligence, Vol. 284. Berlin: Springer, pp. 65-74.
16. Kirtrick, S., Gelatt, C. D. & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, Vol. 220, (4598), pp. 671-680.
17. Geem, Z. W., Kim, J. H. & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony Search. Simulation, Vol. 76, No. 2, pp. 60-68.
18. Osman, K. & Erol, I. E. (2006). A new optimization method: Big Bang-Big Crunch. Adv Eng Software, Vol. 37, No. 2, pp. 106-111.
19. Kaveh, A. & Talatahari, S. (2010). A novel heuristic optimization method: charged system search. Acta Mech, Vol. 213, pp. 267-289.
20. Tamura, K. & Yasuda, K. (2011). Spiral dynamics inspired optimization. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 15, No. 8, pp. 1116-1122.
21. Boussaid, I., Chatterjee, A., Siarry, P. & Ahmed-Nacer, M. (2012). Biogeography-based optimization for constrained optimization problems. Computers & Operations Research, Vol. 39, pp. 3293-3304.
22. Gandomi, A. H. & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simulat, Vol. 17, pp. 4831-4845.
23. Rao, R. V., Savsani, V. J. & Balic, J. (2012). Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Engineering Optimization, DOI:10.1080/0305215X.2011.652103.
24. Kaveh, A. & Khayatazad, M. (2012). A new meta-heuristic method: Ray optimization. Computers and Structures, Vol. 112-113, pp. 283-294.
25. Fister, I., Fister Jr., I., Yang, X. S. & Brest, J. (2013). A comprehensive review of firefly algorithms:, Swarm and Evolutionary Computation, http://dx.doi.org/10.1016/j.swevo.2013.06.001.
26. Kazemzadeh-Parsia, M. J., Daneshmand, F., Ahmadfard, M. A., Adamowski, J. & Martel, R. (2013). Optimal groundwater remediation design of pump and treat systems via a simulation-optimization approach and firefly algorithm. Engineering Optimization, DOI:10.1080/0305215X.2013.858138.
27. Yang, X. S. (2009). Firefly algorithms for multimodal optimization. in: Stochastic Algorithms: Foundations and Applications, SAGA. Lecture Notes in Computer Sciences, Vol. 5792, pp. 169-178.
28. Wang, G., Guo, L., Duan, H., Liu, L. & Wang, H. (2012). A modified firefly algorithm for UCAV path planning. International Journal of Hybrid Information Technology, Vol. 5, No. 3.
29. Coelho, L. D. S. & Mariani, V. C. (2012). Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning. Computers & Mathematics with Applications, Vol. 64, No. 8, pp. 2371- 2382.
30. Gandomi, A. H., Yang, X. S., Talatahari, S. & Alavi, A. H. (2013). Firefly algorithm with chaos. Communications in Nonlinear Science and Numerical Simulation, Vol. 18, No. 1, pp. 89-98.
31. Yang, X. S. (2013). Multi-objective firefly algorithm for continuous optimization. Engineering with Computers, Vol. 29, No. 2, pp. 175-184.
32. Ahmadfard, M. A., Kazemzadeh-Parsi, M. J., Daneshmand, F. & Adamowski, J. (2014). Optimal remediation design of unconfined contaminated aquifers based on the finite element method and a modified version of the firefly algorithm. Submitted to Engineering Optimization.
33. Siddall, J. N. (1972). Analytical decision making in engineering design. Englewood Cliffs: Prentice-Hall.
34. Ragsdell, K. M. & Phillips, D. T. (1976). Optimal design of a class of welded structures using geometric programming. ASME J Eng Ind, Vol. 98, No. 3, pp. 1021-1025.
35. Deb, K. (1991). Optimal design of a welded beam via genetic algorithms. AIAA J, Vol. 29, No. 11, 2013-2015.
36. Leite, J. P. B. & Topping, B. H. V. (1998). Improved genetic operators for structural optimization Adv Eng Software. Vol. 29, No. 7-9, pp. 529-562.
37. Atiqullah, M. M. & Rao, S. S. (2000). Simulated annealing and parallel processing: An implementation for constrained global design optimization. Eng Optimiz, Vol. 32, No. 5, pp. 659-685.
38. Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng, Vol. 186, pp. 311-338.
39. Akhtar, S., Tai, K. & Ray, T. (2002). A socio-behavioural simulation model for engineering design optimization. Eng Optimiz, Vol. 34, No. 4, pp. 341-354.
40. Ray, T. & Liew, K. M. (2003). Society and civilization: An optimization algorithm based on the simulation of social behavior. IEEE Trans Evolution Comput, Vol. 7, No. 4, pp. 386-396.
41. He, S., Prempain, E. & Wu, Q. H. (2004). An improved particle swarm optimizer for mechanical design optimization problems. Eng Optimiz, Vol. 36, No. 5, pp. 585-605.
42. Lemonge, A.C.C. & Barbosa, H.J.C. (2004). An adaptive penalty scheme for genetic algorithms in structural optimization. Int J Numer Methods Eng, Vol. 59, pp. 703-736.
43. Lee, K. S. & Geem, Z. W. (2005). A new metaheuristic algorithm for continues engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng, Vol. 194, pp. 3902-3933.
44. Liu, J. L. (2005). Novel orthogonal simulated annealing with fractional factorial analysis to solve global optimization problems. Eng Optimiz, Vol. 37, No. 5, pp. 499-519.
45. Hedar, A. R. & Fukushima, M. (2006). Derivative-free filter simulated annealing method for constrained continuous global optimization. J Global Optimiz, Vol. 35, 4, pp. 521-649.
46. Zhang, M., Luo, W. & Wang, X. (2008). Differential evolution with dynamic stochastic selection for constrained optimization. Inform Sci, Vol. 178, No. 15, pp. 3043-3074.
47. Zhang, J., Liang, C., Huang, Y., Wu, J. & Yang, S. (2009). An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization. Appl Math Comput, Vol. 211, pp. 392-416.
48. Aragon, V. S., Esquivel, S. C. & Coello, C.A.C. (2010). A modified version of a T-Cell Algorithm for constrained optimization problems. Int. J. Numer. Meth. Engng, Vol. 84, pp. 351-378.
49. Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. ASME J Mech Des, Vol. 112, No. 2, pp. 223-229.
50. Zhang, C. & Wang, H. P. (1993). Mixed-discrete nonlinear optimization with simulated annealing. Engineering Optimization, Vol. 17, No. 3, pp. 263-280.
51. Deb, K. (1997). GeneAS: A robust optimal design technique for mechanical component design. in Evolutionary Algorithms in Engineering Applications, edited by Dasgupta, D., and Michalewicz, Z., Berlin, Springer-Verlage, pp. 497-514.
52. Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Comput Indus, Vol. 41, No. 2, pp. 113-127.
53. Coello, C. A. C. & Montes, E. F. (2002). Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv. Eng. Inf., Vol. 16, pp. 193-203.
54. Mahdavi, M., Fesanghary, M. & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Appl Math Comput, Vol. 188, pp. 1567-1579.
55. Dimopoulos, G. G. (2007). Mixed-variable engineering optimization based on evolutionary and social metaphors. Comput. Methods Appl. Mech. Engrg, Vol. 196, pp. 803-817.
56. Cagnina, L.C., Esquivel, S.C. & Coello, C.A.C. (2008). Solving engineering optimization problems with the simple constrained particle optimizer. Informatica, Vol. 32, pp. 319-26.
57. Shen, H., Zhu, Y., Niu, B. & Wu, Q. H. (2009). An improved group search optimizer for mechanical design optimization problems. Progress in Natural Science, Vol. 19, pp. 91-97.
58. Coelho, L.D.S. (2010). Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Systems with Applications, Vol. 37, pp. 1676-1683.
59. Gandomi, A. H., Yang, X. S. & Alavi, A. H. (2011). Mixed variable structural optimization using Firefly Algorithm. Computers and Structures, Vol. 89, pp. 2325-2336.
60. Gandomi, A. H., Yang, X. S., Alavi, A. H. & Talatahari, S. (2012). Bat algorithm for constrained optimization tasks. Neural Comput & Applic.
61. Ali, M. (2012). Swarm directions embedded differential evolution for faster convergence of global optimization problems. international journal on artificial intelligence tools, Vol. 21, No. 3.
62. Arora, J. S. (1989). Introduction to Optimum Design. New York: McGrow-Hill.
63. Ray, T. & Saini, P. (2001). Engineering design optimization using a swarm with an intelligent information sharing among individuals. Eng Optim, Vol. 33, No. 3, pp. 735-748.
64. Coello, C. A. C. & Becerra, R. L. (2004). Efficient evolutionary optimization through the use of a cultural algorithm. Eng Optim, Vol. 36, pp. 219-236.
65. Parsopoulos, K. E. & Vrahatis, M. N. (2005). Unified particle swarm optimization for solving constrained engineering optimization problems. Lecture Notes in Computer Science, 3612 (LNFA), pp. 582-591.
66. Huang, F. Z., Wang, L. & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Appl. Math. Comput., Vol. 186, pp. 340-356.
67. He, Q. & Wang, L. (2007). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell., Vol. 20, pp. 89-99.
68. Hsu, Y. L. & Liu, T. C. (2007). Developing a fuzzy proportional-derivative controller optimization engine for engineering design opti-mization problems. Eng Optim, Vol. 39, No. 6, pp. 679-700.
69. Rao, S. S. (2011). The finite element method in engineering. Fifth edition, Elsevier, New York.
70. Kaveh, A. & Bakhshpoori, T. (2013). Optimum design of space trusses using cuckoo search algorithm with lévy flights. Iranian Journal Science and Technology, Transactions of Civil Engineering, Vol. 37, No. 1, pp. 1-15.
71. Hatay, T. & Toklu, Y. C. (2002). Optimization of truss using simulated annealing method. Fifth International Congress on Advances in Civil Engineering, Istanbul Technical University, Istanbul, Turkey, 25-27 September, pp. 379-388.
72. Li, L. J., Huang, Z. B., Liu, F. & Wu, Q. H. (2007). A heuristic particle swarm optimizer for optimization of pin connected structures. Computers and Structures, Vol. 85, pp. 340-349.
73. Sonmez, M. (2011). Artificial bee colony algorithm for optimization of truss structures. Applied Soft Computing, Vol. 11, pp. 2406-2418.
74. Imai, K. & Schmit, L.A. (1991). Configuration optimization of trusses. Journal of Structural Division, ASCE, Vol. 107 (ST5), pp.745-756.
75. Lee, K. S. & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers and Structures, Vol. 82, pp. 781-798.
76. Haftka, R. T. & Gürdal, Z. (1992). Elements of structural optimization. 3rd ed., Kluwer Academic Publishers, The Netherlands, pp. 245-250.
77. Lamberti, L. (2008). An efficient simulated annealing algorithm for design optimization of truss structures. Computers and Structures, Vol. 86, pp. 1936-1953.