An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems

Soheyl Khalilpourazari1, Saman Khalilpourazary2
1Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
2Department of Mechanical Engineering, Faculty of Engineering, Urmia University of Technology, Urmia, Iran

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Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intel Manuf 23:1001–14. https://doi.org/10.1007/s10845-010-0393-4

Ali MZ, Awad NH, Suganthan PN, Duwairi RM, Reynolds RG (2016) A novel hybrid Cultural Algorithms framework with trajectory-based search for global numerical optimization. Inf Sci 334:219–249. https://doi.org/10.1016/j.ins.2015.11.032

Arora JS (2004) Introduction to optimum design. Academic Press, London

Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001

Belegundu AD (1983) Study of mathematical programming methods for structural optimization. Diss Abstr Int Part B Sci Eng 43

Cerný V (1985) Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Optim Theory Appl 45:41–51. https://doi.org/10.1007/BF00940812

Coelho LDS (2010) Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Syst Appl 37:1676–1683. https://doi.org/10.1016/j.eswa.2009.06.044

Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41:113–127. https://doi.org/10.1016/S0166-3615(99)00046-9

Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287. https://doi.org/10.1016/S0045-7825(01)00323-1

Coello CA, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16:193–203. https://doi.org/10.1016/S1474-0346(02)00011-3

Digalakis J, Margaritis K (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77:481–506. https://doi.org/10.1080/00207160108805080

Du H, Wu X, Zhuang J (2006) Small-world optimization algorithm for function optimization. In: Jiao L, Wang L, Gao X, Liu J, Wu F (eds) Advances in natural computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_33

Erol OK, Eksin I (2006) New optimization method: big bang–big crunch. Adv Eng Soft 37:106–111. https://doi.org/10.1016/j.advengsoft.2005.04.005

Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm—a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166. https://doi.org/10.1016/j.compstruc.2012.07.010

Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, London

Formato RA (2007) Central force optimization: a new metaheuristic with applications in applied electromagnetics. Prog Electromagn Res 77:425–91. https://doi.org/10.2528/PIER07082403

Frank KD, Rich C, Longcore T (2006) Effects of artificial night lighting on moths. In: Ecological consequences of artificial night lighting, Island Press, USA, pp 305–344

Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35. https://doi.org/10.1007/s00366-011-0241-y

Gao XZ, Wang X, Jokinen T, Ovaska SJ, Arkkio A, Zenger K (2012) A hybrid optimization method for wind generator design. Int J Innov Comput Inf Control 8:4347–4373

Gaston KJ, Bennie J, Davies TW, Hopkins J (2013) The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biol Rev 88(2013):912–927. https://doi.org/10.1111/brv.12036

Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184. https://doi.org/10.1016/j.ins.2012.08.023

He Q, Wang L (2007a) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407–1422. https://doi.org/10.1016/j.amc.2006.07.134

He Q, Wang L (2007b) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99. https://doi.org/10.1016/j.engappai.2006.03.003

Holland JH (1992) Genetic algorithms. Sci Am 267:66–72

Huang FZ, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186:340–356. https://doi.org/10.1016/j.amc.2006.07.105

Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Opt 39:459–471. https://doi.org/10.1007/s10898-007-9149-x

Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Soft 110:69–84. https://doi.org/10.1016/j.advengsoft.2017.03.014

Kaveh A, Khayatazad M (2012) A new meta-heuristic method: ray optimization. Comput Struct 112:283–94. https://doi.org/10.1016/j.compstruc.2012.09.003

Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289. https://doi.org/10.1007/s00707-009-0270-4

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1948

Khalilpourazari S, Khalilpourazary S (2016) Optimization of production time in the multi-pass milling process via a Robust Grey Wolf Optimizer. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2644-6

Khalilpourazari S, Khalilpourazary S (2017a) A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization of the surface grinding process. Eng Optim 49:878–895. https://doi.org/10.1080/0305215X.2016.1214437

Khalilpourazari S, Khamseh AA (2017) Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Ann Oper Res. https://doi.org/10.1007/s10479-017-2588-y

Khalilpourazari S, Pasandideh SHR (2017) Multi-item EOQ model with nonlinear unit holding cost and partial backordering: moth-flame optimization algorithm. J Ind Prod Eng 34:42–51. https://doi.org/10.1080/21681015.2016.1192068

Khalilpourazari S, Khalilpourazary S (2017b) A Robust Stochastic Fractal Search approach for optimization of the surface grinding process. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2017.07.008

Khalilpourazari S, Mohammadi M (2016) Optimization of closed-loop Supply chain network design: a water cycle algorithm approach. In: 2th international conference on industrial engineering. IEEE, pp 41–45. https://doi.org/10.1109/INDUSENG.2016.7519347

Khalilpourazari S, Pasandideh SHR (2016) Bi-objective optimization of multi-product EPQ model with backorders, rework process and random defective rate. In: 2th international conference on industrial engineering. IEEE, pp 36–40. https://doi.org/10.1109/INDUSENG.2016.7519346

Khalilpourazary S, Kashtiban PM, Payam N (2014a) Optimization of turning operation of St37 steel using grey relational analysis. J Comput Appl Res Mech Eng 3:135–144. https://doi.org/10.22061/JCARME.2014.67

Khalilpourazary S, Abdi Behnagh R, Mahdavinejad RA, Payam N (2014b) Dissimilar friction stir lap welding of Al–Mg to CuZn34: application of grey relational analysis for optimization of process parameters. J Comput Appl Res Mech Eng 4:81–88. https://doi.org/10.22061/JCARME.2014.74

Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simmulated annealing. Science 220:671–680

Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Soft 92:65–88. https://doi.org/10.1016/j.advengsoft.2015.11.004

Liu C, Linan F (2016) A hybrid evolutionary algorithm based on tissue membrane systems and CMA-ES for solving numerical optimization problems. Knowl Based Sys 105:38–47. https://doi.org/10.1016/j.knosys.2016.04.025

Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640. https://doi.org/10.1016/j.asoc.2009.08.031

Mirjalili S (2015a) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Sys 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

Mirjalili S (2015b) The ant lion optimizer. Adv Eng Soft 83:80–98. https://doi.org/10.1016/j.advengsoft.2015.01.010

Mirjalili S (2016a) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete and multi-objective problems. Neural Comput Appl 27:1053–1073. https://doi.org/10.1007/s00521-015-1920-1

Mirjalili S (2016b) SCA: a sine cosine algorithm for solving optimization problems. Knowled Based Sys 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Soft 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Soft 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495–513. https://doi.org/10.1007/s00521-015-1870-7

Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. In: Computer and information application (ICCIA). IEEE, pp 374–377. https://doi.org/10.1109/ICCIA.2010.6141614

Moghaddam FF, Moghaddam RF, Cheriet M (2012) Curved space optimization: a random search based on general relativity theory. arXiv preprint arXiv:1208.2214

Molga M, Smutnicki C (2005) Test functions for optimization needs, p 101

Parsopoulos K, Vrahatis M (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Advances in natural computation, pp 582–591. https://doi.org/10.1007/11539902_71

Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315. https://doi.org/10.1016/j.cad.2010.12.015

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

Rechenberg I (1978) Evolutionsstrategien. Springer, Berlin

Sadollah A, Bahreininejad A, Eskandar H, Hamdi M (2013) Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems. Appl Soft Comput 13:2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026

Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015a) Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures. Comput Struct 149:1–16. https://doi.org/10.1016/j.compstruc.2014.12.003

Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015b) Water cycle algorithm for solving multi-objective optimization problems. Soft Comput 19:2587–2603. https://doi.org/10.1007/s00500-014-1424-4

Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015c) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58–71

Sadollah A, Eskandar H, Kim JH (2015d) Water cycle algorithm for solving constrained multi-objective optimization problems. Appl Soft Comput 27:279–298. https://doi.org/10.1016/j.asoc.2014.10.042

Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl Based Sys 75:1–18. https://doi.org/10.1016/j.knosys.2014.07.025

Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimization algorithm: theory and application. Adv Eng Soft 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004

Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713. https://doi.org/10.1109/TEVC.2008.919004

Topal AO, Altun O (2016) A novel meta-heuristic algorithm: dynamic virtual bats algorithm. Inf Sci 354:222–235. https://doi.org/10.1016/j.ins.2016.03.025

Wang G, Guo L (2013) A novel hybrid bat algorithm with harmony search for global numerical optimization. J Appl Math. https://doi.org/10.1155/2013/696491

Wang GG, Guo L, Gandomi AH, Hao GS, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34. https://doi.org/10.1016/j.ins.2014.02.123

Wang GG, Gandomi AH, Alavi AH (2014b) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38:2454–2462. https://doi.org/10.1016/j.apm.2013.10.052

Wang GG, Gandomi AH, Alavi AH, Hao GS (2014c) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25:297–308. https://doi.org/10.1007/s00521-013-1485-9

Wang GG, Gandomi AH, Zhao X, Chu HC (2016) Hybridizing harmony search algorithm with cuckoo search for global numerical optimization. Soft Comput 20:273–285. https://doi.org/10.1007/s00500-014-1502-7

Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on IEEE nature & biologically inspired computing. https://doi.org/10.1109/NABIC.2009.5393690

Yang X-S (2010a) Appendix A: test problems in optimization. In: Engineering optimization, Wiley, Hoboken, NJ, USA. https://doi.org/10.1002/9780470640425.app1

Yang XS (2010b) Nature-inspired metaheuristic algorithms. Luniver Press, London

Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102. https://doi.org/10.1109/4235.771163

Zareh-Desari B, Abaszadeh-Yakhforvazani M, Khalilpourazary S (2015) The effect of nanoparticle additives on lubrication performance in deep drawing process: evaluation of forming load, friction coefficient and surface quality. Int J Precis Eng Manuf 16:929–936. https://doi.org/10.1007/s12541-015-0121-2