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

Computers & Structures - Tập 110-111 - Trang 151-166 - 2012
Hadi Eskandar1, Ali Sadollah2, Ardeshir Bahreininejad2, M. Hamdi2
1Faculty of Engineering, Semnan University, Semnan, Iran#TAB#
2Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia#TAB#

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Tài liệu tham khảo

Lee, 2005, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, Comput Meth Appl Mech Eng, 194, 3902, 10.1016/j.cma.2004.09.007

Holland, 1975

Goldberg, 1989

Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. Perth, Australia: 1995. p. 1942–8.

Kirkpatrick, 1983, Optimization by simulated annealing, Science, 220, 671, 10.1126/science.220.4598.671

Coello, 2002, Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art, Comput Meth Appl Mech Eng, 191, 1245, 10.1016/S0045-7825(01)00323-1

Areibi, 2001

Elbeltagi, 2005, Comparison among five evolutionary-based optimization algorithms, Adv Eng Inf, 19, 43, 10.1016/j.aei.2005.01.004

Youssef, 2001, Evolutionary algorithms, simulated annealing and tabu search: a comparative study, Eng Appl Artif Intell, 14, 167, 10.1016/S0952-1976(00)00065-8

Giraud-Moreau, 2002, Comparison of evolutionary algorithms for mechanical design components, Eng Optim, 34, 307, 10.1080/03052150211750

Chootinan, 2006, Constraint handling in genetic algorithms using a gradient-based repair method, Comput Oper Res, 33, 2263, 10.1016/j.cor.2005.02.002

Trelea, 2003, The particle swarm optimization algorithm: convergence analysis and parameter selection, Inform Process Lett, 85, 317, 10.1016/S0020-0190(02)00447-7

He, 2006, An effective co-evolutionary particle swarm optimization for engineering optimization problems, Eng Appl Artif Intell, 20, 89, 10.1016/j.engappai.2006.03.003

Gomes, 2011, Truss optimization with dynamic constraints using a particle swarm algorithm, Expert Syst Appl, 38, 957, 10.1016/j.eswa.2010.07.086

David, 1993

Strahler, 1952, Dynamic basis of geomorphology, Geol Soc Am Bull, 63, 923, 10.1130/0016-7606(1952)63[923:DBOG]2.0.CO;2

Montes, 2008, An empirical study about the usefulness of evolution strategies to solve constrained optimization problems, Int J Gen Syst, 37, 443, 10.1080/03081070701303470

Kaveh, 2009, A particle swarm ant colony optimization for truss structures with discrete variables, J Const Steel Res, 65, 1558, 10.1016/j.jcsr.2009.04.021

Koziel, 1999, Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization, IEEE Trans Evol Comput, 7, 19

Ben Hamida, 2002, ASCHEA: new results using adaptive segregational constraint handling, IEEE Trans Evol Comput, 884

Tang, 2011, An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems, Comput Chem Eng, 35, 615, 10.1016/j.compchemeng.2010.06.014

Michalewicz, 1995, Genetic algorithms, numerical optimization, and constraints, 151

Mezura-Montes, 2005, A simple multimembered evolution strategy to solve constrained optimization problems, IEEE Trans Evol Comput, 9, 1, 10.1109/TEVC.2004.836819

Tessema, 2006, A self adaptive penalty function based algorithm for constrained optimization, IEEE Trans Evol Comput, 246, 10.1109/CEC.2006.1688315

Liu, 2010, Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization, Appl Soft Comput, 10, 629, 10.1016/j.asoc.2009.08.031

Runarsson, 2000, Stochastic ranking for constrained evolutionary optimization, IEEE Trans Evol Comput, 4, 284, 10.1109/4235.873238

Lampinen, 2002, A constraint handling approach for the differential evolution algorithm, IEEE Trans Evol Comput, 1468

Becerra, 2006, Cultured differential evolution for constrained optimization, Comput Meth Appl Mech Eng, 195, 4303, 10.1016/j.cma.2005.09.006

Renato, 2006, Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems, IEEE Trans Syst Man Cybern Part B Cybern, 36, 1407, 10.1109/TSMCB.2006.873185

Zhang, 2008, Differential evolution with dynamic stochastic selection for constrained optimization, Inform Sci, 178, 3043, 10.1016/j.ins.2008.02.014

Runarsson, 2005, Search biases in constrained evolutionary optimization, IEEE Trans Syst Man Cybern Part C Appl Rev, 35, 233, 10.1109/TSMCC.2004.841906

Wang, 2009, Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint handling technique, Struct Multidisc Optim, 37, 395, 10.1007/s00158-008-0238-3

Takahama, 2005, Constrained optimization by applying the α; constrained method to the nonlinear simplex method with mutations, IEEE Trans Evol Comput, 9, 437, 10.1109/TEVC.2005.850256

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.

Huang, 2007, An effective co-evolutionary differential evolution for constrained optimization, Appl Math Comput, 186, 340, 10.1016/j.amc.2006.07.105

Karaboga, 2007, Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, LNAI, 4529, 789

Coello, 2004, Efficient evolutionary optimization through the use of a cultural algorithm, Eng Optim, 36, 219, 10.1080/03052150410001647966

He, 2007, A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization, Appl Math Comput, 186, 1407, 10.1016/j.amc.2006.07.134

Amirjanov, 2006, The development of a changing range genetic algorithm, Comput Meth Appl Mech Eng, 195, 2495, 10.1016/j.cma.2005.05.014

Wang, 2010, An effective differential evolution with level comparison for constrained engineering design, Struct Multidisc Optim, 41, 947, 10.1007/s00158-009-0454-5

Zahara, 2009, Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems, Expert Syst Appl, 36, 3880, 10.1016/j.eswa.2008.02.039

Rao, 2011, Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems, Comput Aided Des, 43, 303, 10.1016/j.cad.2010.12.015

Ray, 2003, Society and civilization: an optimization algorithm based on the simulation of social behavior, IEEE Trans Evol Comput, 7, 386, 10.1109/TEVC.2003.814902

Mezura-Montes, 2005, Useful infeasible solutions in engineering optimization with evolutionary algorithms, MICAI 2005, Lect Notes Artif Int, 3789, 652

Montes E, Reyes JV, Coello CAC. Modified differential evolution for constrained optimization. In: IEEE congress on evolutionary computation. CEC; 2006a. p. 25–32.

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.

Kannan, 1994, An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design, J Mech Des, 116, 405, 10.1115/1.2919393

Coello, 2000, Use of a self-adaptive penalty approach for engineering optimization problems, Comput Ind, 41, 113, 10.1016/S0166-3615(99)00046-9

C Coello, 2002, Constraint-handling in genetic algorithms through the use of dominance-based tournament selection, Adv Eng Inf, 16, 193, 10.1016/S1474-0346(02)00011-3

Coelho, 2010, Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems, Expert Syst Appl, 37, 1676, 10.1016/j.eswa.2009.06.044

Arora, 1989

Yuan, 2010, A hybrid genetic algorithm for twice continuously differentiable NLP problems, Comput Chem Eng, 34, 36, 10.1016/j.compchemeng.2009.09.006

Coello, 2000, Constraint-handling using an evolutionary multiobjective optimization technique, Civ Eng Environ Syst, 17, 319, 10.1080/02630250008970288

Gupta, 2007, Multi-objective design optimization of rolling bearings using genetic algorithm, Mech Mach Theory, 42, 1418, 10.1016/j.mechmachtheory.2006.10.002

Osyczka, 2002

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.