Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems

Computers and Structures

Volumn 110-111, Issue , 2012, Pages 151-166

  Eskandar, Hadi ^a   Sadollah, Ali ^b   Bahreininejad, Ardeshir ^b   Hamdi, Mohd ^b  

^a SEMNAN UNIVERSITY   (Iran)

^b UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Constrained problems; Constraint handling; Engineering design; Global optimization; Metaheuristic; Water cycle algorithm

Indexed keywords

CONSTRAINED PROBLEM; CONSTRAINT HANDLING; ENGINEERING DESIGN; METAHEURISTIC; WATER CYCLE;

ALGORITHMS; GLOBAL OPTIMIZATION;

CONSTRAINED OPTIMIZATION;

EID: 84865862850     PISSN: 00457949     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compstruc.2012.07.010     Document Type: Article

References (56)

1. K.S. Lee, and Z.W. Geem A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice Comput Meth Appl Mech Eng 194 2005 3902 3933
2. J. Holland Adaptation in natural and artificial systems 1975 University of Michigan Press Ann Arbor, MI
3. D. Goldberg Genetic algorithms in search, optimization and machine learning 1989 Addison-Wesley Reading, MA
4. Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Perth, Australia: 1995. p. 1942-8.
5. S. Kirkpatrick, C. Gelatt, and M. Vecchi Optimization by simulated annealing Science 220 1983 671 680
6. C.A.C. Coello Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art Comput Meth Appl Mech Eng 191 2002 1245 1287
7. S. Areibi, M. Moussa, and H. Abdullah A comparison of genetic/memetic algorithms and other heuristic search techniques 2001 ICAI Las VeGAs, Nevada
8. E. Elbeltagi, T. Hegazy, and D. Grierson Comparison among five evolutionary-based optimization algorithms Adv Eng Inf 19 2005 43 53
9. H. Youssef, S.M. Sait, and H. Adiche Evolutionary algorithms, simulated annealing and tabu search: a comparative study Eng Appl Artif Intell 14 2001 167 181
10. L. Giraud-Moreau, and P. Lafon Comparison of evolutionary algorithms for mechanical design components Eng Optim 34 2002 307 322
11. P. Chootinan, and A. Chen Constraint handling in genetic algorithms using a gradient-based repair method Comput Oper Res 33 2006 2263 2281
12. I.C. Trelea The particle swarm optimization algorithm: convergence analysis and parameter selection Inform Process Lett 85 2003 317 325
13. Q. He, and L. Wang An effective co-evolutionary particle swarm optimization for engineering optimization problems Eng Appl Artif Intell 20 2006 89 99
14. H.M. Gomes Truss optimization with dynamic constraints using a particle swarm algorithm Expert Syst Appl 38 2011 957 968
15. S. David The water cycle, illustrations by John Yates 1993 Thomson Learning New York
16. A.N. Strahler Dynamic basis of geomorphology Geol Soc Am Bull 63 1952 923 938
17. E.M. Montes, and C.A.C. Coello An empirical study about the usefulness of evolution strategies to solve constrained optimization problems Int J Gen Syst 37 2008 443 473
18. A. Kaveh, and S. Talatahari A particle swarm ant colony optimization for truss structures with discrete variables J Const Steel Res 65 2009 1558 1568
19. S. Koziel, and Z. Michalewicz Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization IEEE Trans Evol Comput 7 1999 19 44
20. S. Ben Hamida, and M. Schoenauer ASCHEA: new results using adaptive segregational constraint handling IEEE Trans Evol Comput 2002 884 889
21. K.Z. Tang, T.K. Sun, and J.Y. Yang An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems Comput Chem Eng 35 2011 615 621
22. Z. Michalewicz Genetic algorithms, numerical optimization, and constraints L. Esheman, Proceedings of the sixth international conference on genetic algorithms 1995 Morgan Kauffman San Mateo 151 158
23. E. Mezura-Montes, and C.A.C. Coello A simple multimembered evolution strategy to solve constrained optimization problems IEEE Trans Evol Comput 9 2005 1 17
24. B. Tessema, and G.G. Yen A self adaptive penalty function based algorithm for constrained optimization IEEE Trans Evol Comput 2006 246 253
25. H. Liu, Z. Cai, and Y. Wang Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization Appl Soft Comput 10 2010 629 640
26. T.P. Runarsson, and Y. Xin Stochastic ranking for constrained evolutionary optimization IEEE Trans Evol Comput 4 2000 284 294
27. J. Lampinen A constraint handling approach for the differential evolution algorithm IEEE Trans Evol Comput 2002 1468 1473
28. R. Becerra, and C.A.C. Coello Cultured differential evolution for constrained optimization Comput Meth Appl Mech Eng 195 2006 4303 4322
29. A.K. Renato, and C. Leandro Dos Santos Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems IEEE Trans Syst Man Cybern Part B Cybern 36 2006 1407 1416
30. M. Zhang, W. Luo, and X. Wang Differential evolution with dynamic stochastic selection for constrained optimization Inform Sci 178 2008 3043 3074
31. T.P. Runarsson, and Y. Xin Search biases in constrained evolutionary optimization IEEE Trans Syst Man Cybern Part C Appl Rev 35 2005 233 243
32. Y. Wang, Z. Cai, Y. Zhou, and Z. Fan Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint handling technique Struct Multidisc Optim 37 2009 395 413
33. T. Takahama, and S. Sakai Constrained optimization by applying the α; constrained method to the nonlinear simplex method with mutations IEEE Trans Evol Comput 9 2005 437 451
34. A.E.M. Zavala, A.H. Aguirre, E.R.V. Diharce, Constrained optimization via evolutionary swarm optimization algorithm (PESO). In: Proceedings of the 2005 conference on genetic and evolutionary computation. New York, USA: 2005. p. 209-16.
35. F.Z. Huang, L. Wang, and Q. He An effective co-evolutionary differential evolution for constrained optimization Appl Math Comput 186 1 2007 340 356
36. D. Karaboga, and B. Basturk Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems LNAI 4529 2007 789 798 [Berlin: Springer-Verlag]
37. C.A.C. Coello, and R.L. Becerra Efficient evolutionary optimization through the use of a cultural algorithm Eng Optim 36 2004 219 236
38. Q. He, and L. Wang A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization Appl Math Comput 186 2007 1407 1422
39. A. Amirjanov The development of a changing range genetic algorithm Comput Meth Appl Mech Eng 195 2006 2495 2508
40. L. Wang, and L.P. Li An effective differential evolution with level comparison for constrained engineering design Struct Multidisc Optim 41 2010 947 963
41. E. Zahara, and Y.T. Kao Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems Expert Syst Appl 36 2009 3880 3886
42. R.V. Rao, V.J. Savsani, and D.P. Vakharia Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems Comput Aided Des 43 2011 303 315
43. T. Ray, and K.M. Liew Society and civilization: an optimization algorithm based on the simulation of social behavior IEEE Trans Evol Comput 7 2003 386 396
44. E. Mezura-Montes, and C.A.C. Coello Useful infeasible solutions in engineering optimization with evolutionary algorithms, MICAI 2005 Lect Notes Artif Int 3789 2005 2005 652 662
45. Montes E, Reyes JV, Coello CAC. Modified differential evolution for constrained optimization. In: IEEE congress on evolutionary computation. CEC; 2006a. p. 25-32.
46. Montes E, Coello CAC, Reyes JV. Increasing successful offspring and diversity in differential evolution for engineering design. In: Proceedings of the seventh international conference on adaptive computing in design and manufacture. 2006b. p. 131-9.
47. B.K. Kannan, and S.N. Kramer An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design J Mech Des 116 1994 405 411
48. C.A.C. Coello Use of a self-adaptive penalty approach for engineering optimization problems Comput Ind 41 2000 113 127
49. C.A. C Coello, and E. Mezura Montes Constraint-handling in genetic algorithms through the use of dominance-based tournament selection Adv Eng Inf 16 2002 193 203
50. L.D.S. Coelho Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems Expert Syst Appl 37 2010 1676 1683
51. J.S. Arora Introduction to optimum design 1989 McGraw-Hill New York
52. Q. Yuan, and F. Qian A hybrid genetic algorithm for twice continuously differentiable NLP problems Comput Chem Eng 34 2010 36 41
53. C.A.C. Coello Constraint-handling using an evolutionary multiobjective optimization technique Civ Eng Environ Syst 17 2000 319 346
54. S. Gupta, R. Tiwari, and B.N. Shivashankar Multi-objective design optimization of rolling bearings using genetic algorithm Mech Mach Theory 42 2007 1418 1443
55. A. Osyczka Evolutionary algorithms for single and multicriteria design optimization: studies in fuzzyness and soft computing 2002 Physica-Verlag Heidelberg
56. Deb K, Srinivasan A. Innovization: innovative design principles through optimization, Kanpur genetic algorithms laboratory (KanGAL). Indian Institute of Technology Kanpur, KanGAL report number: 2005007; 2005.