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Advanced Metaheuristic Techniques for Mechanical Design Problems: Review

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Abstract

The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter spaces. There is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. Nowadays, there is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. This review article demonstrates the applications of different metaheuristic algorithms in enhancing the design process of different mechanical systems. First, the basic concepts of common used metaheuristic algorithms are introduced. Then the applications of theses algorithms in optimization of different mechanical systems are discussed.

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Abbreviations

ALO:

Ant-lion optimizer

ABC:

Artificial Bee Colony

CGO:

Chaos Game Optimization

CSA:

Cuckoo Search algorithm

DBE:

Decomposition-based evolutionary

DE:

Differential evolution

EO:

Equilibrium optimizer

GA:

Genetic algorithms

GSA:

Gravitational search algorithm

GWO:

Grey Wolf Optimization

MBA:

Mine Blast Algorithm

MFO:

Moth-flame optimization

PSO:

Particle swarm optimization

QEA:

Quantum-inspired Evolutionary Algorithm

SA:

Simulated annealing

TLBO:

Teaching learning-based optimization

TEO:

Thermal exchange optimization algorithm

TSA:

Tunicate swarm algorithm

WEO:

Water evaporation optimization algorithm

ER-WCA:

Water cycle algorithm evaporation rate

WOA:

Whale optimization algorithm

References

  1. Cheng M-Y, Prayogo D (2014) Symbiotic Organisms Search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Article  Google Scholar 

  2. Oliva D, Elaziz MA, Elsheikh AH, Ewees AA (2019) A review on meta-heuristics methods for estimating parameters of solar cells. J Power Sources 435:126683

    Article  Google Scholar 

  3. Ransegnola T, Zhao X, Vacca A (2019) A comparison of helical and spur external gear machines for fluid power applications: design and optimization. Mech Mach Theory 142:103604

    Article  Google Scholar 

  4. Cui D, Wang G, Lu Y, Sun K (2020) Reliability design and optimization of the planetary gear by a GA based on the DEM and Kriging model. Reliab Eng Syst Saf 203:107074

    Article  Google Scholar 

  5. Fei C, Liu H, Zhu Z, An L, Li S, Lu C (2020) Whole-process design and experimental validation of landing gear lower drag stay with global/local linked driven optimization strategy. Chin J Aeronaut 34:318–328

    Article  Google Scholar 

  6. Jahangiri M, Hadianfard MA, Najafgholipour MA, Jahangiri M, Gerami MR (2020) Interactive autodidactic school: a new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems. Comput Struct 235:106268

    Article  Google Scholar 

  7. Zayed ME, Zhao J, Elsheikh AH, Li W, Elaziz MA (2020) Optimal design parameters and performance optimization of thermodynamically balanced dish/Stirling concentrated solar power system using multi-objective particle swarm optimization. Appl Therm Eng 178:115539

    Article  Google Scholar 

  8. Elsheikh AH, Deng W, Showaib EA (2020) Improving laser cutting quality of polymethylmethacrylate sheet: experimental investigation and optimization. J Market Res 9:1325–1339

    Google Scholar 

  9. Millo F, Arya P, Mallamo F (2018) Optimization of automotive diesel engine calibration using genetic algorithm techniques. Energy 158:807–819

    Article  Google Scholar 

  10. Sun G, Tian J, Liu T, Yan X, Huang X (2018) Crashworthiness optimization of automotive parts with tailor rolled blank. Eng Struct 169:201–215

    Article  Google Scholar 

  11. Grefenstette JJ (1993) August. Genetic algorithms and machine learning. In: Proceedings of the sixth annual conference on Computational learning theory, pp 3–4

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

    Article  MathSciNet  MATH  Google Scholar 

  13. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, pp 1942–1948

  14. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization, Technical report-tr06. Erciyes University, Engineering Faculty, Computer

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

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  18. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  19. Price KV (1996) Differential evolution: a fast and simple numerical optimizer. In: Proceedings of North American fuzzy information processing, pp 524–527

  20. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  21. Asafuddoula M, Ray T, Sarker R (2014) A decomposition-based evolutionary algorithm for many objective optimization. IEEE Trans Evol Comput 19:445–460

    Article  MATH  Google Scholar 

  22. Han K-H, Kim J-H (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6:580–593

    Article  Google Scholar 

  23. Hinterding R (1999) Representation, constraint satisfaction and the knapsack problem. In: Proceedings of the 1999 Congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), pp 1286–1292

  24. Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International conference on parallel problem solving from nature, pp 849–858

  25. Hof PR, Van der Gucht E (2007) Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae (Cetacea, Mysticeti, Balaenopteridae). Anat Rec 290:1–31

    Article  Google Scholar 

  26. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  27. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  28. Marjanovic N, Isailovic B, Marjanovic V, Milojevic Z, Blagojevic M, Bojic M (2012) A practical approach to the optimization of gear trains with spur gears. Mech Mach Theory 53:1–16

    Article  Google Scholar 

  29. Armillotta A (2019) Tolerance analysis of gear trains by static analogy. Mech Mach Theory 135:65–80

    Article  Google Scholar 

  30. Li X, Wang A (2019) A modularization method of dynamic system modeling for multiple planetary gear trains transmission gearbox. Mech Mach Theory 136:162–177

    Article  Google Scholar 

  31. Mendi F, Başkal T, Boran K, Boran FE (2010) Optimization of module, shaft diameter and rolling bearing for spur gear through genetic algorithm. Expert Syst Appl 37:8058–8064

    Article  Google Scholar 

  32. Miler D, Žeželj D, Lončar A, Vučković K (2018) Multi-objective spur gear pair optimization focused on volume and efficiency. Mech Mach Theory 125:185–195

    Article  Google Scholar 

  33. Wang H, Zou Z (2011) Design of optimization of gear train weight based on reliability simulated annealing. In: Proceedings 2011 international conference on transportation, mechanical, and electrical engineering (TMEE), pp 883–886

  34. Tamboli K, Patel S, George PM, Sanghvi R (2014) Optimal design of a heavy duty helical gear pair using particle swarm optimization technique. Proc Technol 14:513–519

    Article  Google Scholar 

  35. Savsani V, Rao RV, Vakharia DP (2010) Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms. Mech Mach Theory 45:531–541

    Article  MATH  Google Scholar 

  36. Lampinen J (2003) Cam shape optimisation by genetic algorithm. Comput Aided Des 35:727–737

    Article  Google Scholar 

  37. Tsiafis I, Mitsi S, Bouzakis K, Papadimitriou A (2013) Optimal design of a cam mechanism with translating flat-face follower using genetic algorithm. Tribol Ind 35:255–260

    Google Scholar 

  38. Fang R, Chen H (2010) Research on cam curve optimal design based on genetic algorithm. In: 2010 second international conference on computer modeling and simulation, pp 249–252

  39. Qin W, He J (2010) Optimum design of local cam profile of a valve train. Proc Inst Mech Eng C J Mech Eng Sci 224:2487–2492

    Article  Google Scholar 

  40. Ge RY, Guo P (2012) Flexible cam profile synthesis method using NURBS and its optimization based on genetic algorithm. In: Advanced materials research, pp 69–72

  41. Mandal M, Naskar TK (2009) Introduction of control points in splines for synthesis of optimized cam motion program. Mech Mach Theory 44:255–271

    Article  MATH  Google Scholar 

  42. Zhi L, Zhansheng L, Yigong L (2005) Dynamic simulation of distribution cam mechanism in internal combustion engine based on ant colony algorithm. Trans Chin Soc Agric Eng 6

  43. Bravo HR, Flocker WF (2011) Optimizing cam profiles using the particle swarm technique. ASME. J Mech Des 133(9):091003

    Article  Google Scholar 

  44. Abderazek H, Yildiz AR, Mirjalili S (2020) Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism. Knowl-Based Syst 191:105237

    Article  Google Scholar 

  45. Sessarego M, Feng J, Ramos-García N, Horcas SG (2020) Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow. Renew Energy 146:1524–1535

    Article  Google Scholar 

  46. Keshavarzzadeh V, Ghanem RG, Tortorelli DA (2019) Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines. Comput Methods Appl Mech Eng 354:271–306

    Article  MathSciNet  MATH  Google Scholar 

  47. Kear M, Evans B, Ellis R, Rolland S (2016) Computational aerodynamic optimisation of vertical axis wind turbine blades. Appl Math Model 40:1038–1051

    Article  MathSciNet  MATH  Google Scholar 

  48. Chan CM, Bai HL, He DQ (2018) Blade shape optimization of the Savonius wind turbine using a genetic algorithm. Appl Energy 213:148–157

    Article  Google Scholar 

  49. Jureczko M, Pawlak M, Mężyk A (2005) Optimisation of wind turbine blades. J Mater Process Technol 167:463–471

    Article  Google Scholar 

  50. Selig MS, Coverstone-Carroll VL (1996) Application of a genetic algorithm to wind turbine design. J Energy Res Technol 118:22–28

    Article  Google Scholar 

  51. Maral H, Alpman E, Kavurmacıoğlu L, Camci C (2019) A genetic algorithm based aerothermal optimization of tip carving for an axial turbine blade. Int J Heat Mass Transf 143:118419

    Article  Google Scholar 

  52. Vianna Neto JX, Guerra Junior EJ, Moreno SR, Hultmann Ayala HV, Mariani VC, Coelho LS (2018) Wind turbine blade geometry design based on multi-objective optimization using metaheuristics. Energy 162:645–658

    Article  Google Scholar 

  53. Ceruti A (2019) Meta-heuristic multidisciplinary design optimization of wind turbine blades obtained from circular pipes. Eng Comput 35:363–379

    Article  Google Scholar 

  54. Ma Y, Zhang A, Yang L, Hu C, Bai Y (2019) Investigation on optimization design of offshore wind turbine blades based on particle swarm optimization. Energies 12:1972

    Article  Google Scholar 

  55. Boeing, Boeing: commercial airplanes—747 fun facts. https://www.boeing.com/commercial/747/. Accessed Sept 2020

  56. Chattot J-J (2004) Computational aerodynamics and fluid dynamics: an introduction. Springer, Berlin

    MATH  Google Scholar 

  57. Li M, Bai J, Li L, Meng X, Liu Q, Chen B (2019) A gradient-based aero-stealth optimization design method for flying wing aircraft. Aerosp Sci Technol 92:156–169

    Article  Google Scholar 

  58. Skinner SN, Zare-Behtash H (2018) State-of-the-art in aerodynamic shape optimisation methods. Appl Soft Comput 62:933–962

    Article  Google Scholar 

  59. Allen Gardner B, Michael S (2003) Airfoil design using a genetic algorithm and an inverse method. In: AIAA, vol. 200320043

  60. Khurana M, Winarto H, Sinha A (2008) Airfoil geometry parameterization through shape optimizer and computational fluid dynamics. In: 46th AIAA aerospace sciences meeting and exhibit, p 295

  61. Wang Y-y, Zhang B-q, Chen Y-c (2011) Robust airfoil optimization based on improved particle swarm optimization method. Appl Math Mech 32:1245

    Article  MathSciNet  MATH  Google Scholar 

  62. Wickramasinghe UK, Carrese R, Li X (2010) Designing airfoils using a reference point based evolutionary many-objective particle swarm optimization algorithm. In: IEEE congress on evolutionary computation, pp 1–8

  63. Tao J, Sun G, Wang X, Guo L (2019) Robust optimization for a wing at drag divergence Mach number based on an improved PSO algorithm. Aerosp Sci Technol 92:653–667

    Article  Google Scholar 

  64. Koreanschi A, Sugar Gabor O, Acotto J, Brianchon G, Portier G, Botez RM et al (2017) Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, part I—aerodynamic optimization using genetic, bee colony and gradient descent algorithms. Chin J Aeron 30:149–163

    Article  Google Scholar 

  65. Hashimoto A, Jeong S, Obayashi S (2015) Aerodynamic optimization of near-future high-wing aircraft. Trans Jpn Soc Aeronaut Space Sci 58:73–82

    Article  Google Scholar 

  66. Sasaki D, Obayashi S (2005) Efficient search for trade-offs by adaptive range multi-objective genetic algorithms. J Aerosp Comput Inf Commun 2:44–64

    Article  Google Scholar 

  67. Chiba K, Oyama A, Obayashi S, Nakahashi K, Morino H (2007) Multidisciplinary design optimization and data mining for transonic regional-jet wing. J Aircr 44:1100–1112

    Article  Google Scholar 

  68. Sasaki D, Morikawa M, Obayashi S, Nakahashi K (2001) Aerodynamic shape optimization of supersonic wings by adaptive range multiobjective genetic algorithms. In: International conference on evolutionary multi-criterion optimization, pp 639–652

  69. Gil AV, Zavorin AS, Starchenko AV (2019) Numerical investigation of the combustion process for design and non-design coal in T-shaped boilers with swirl burners. Energy 186:115844

    Article  Google Scholar 

  70. Xie Y, Tu Y, Jin H, Luan C, Wang Z, Liu H (2019) Numerical study on a novel burner designed to improve MILD combustion behaviors at the oxygen enriched condition. Appl Therm Eng 152:686–696

    Article  Google Scholar 

  71. Musa O, Xiong C, Weixuan L, Wenhe L (2019) Combustion characteristics of a novel design of solid-fuel ramjet motor with swirl flow. Aerosp Sci Technol 92:750–765

    Article  Google Scholar 

  72. Pantaleo AM, Camporeale SM, Sorrentino A, Miliozzi A, Shah N, Markides CN (2020) Hybrid solar-biomass combined Brayton/organic Rankine-cycle plants integrated with thermal storage: techno-economic feasibility in selected Mediterranean areas. Renew Energy 147:2913–2931

    Article  Google Scholar 

  73. Smith JD, Sreedharan V, Landon M, Smith ZP (2020) Advanced design optimization of combustion equipment for biomass combustion. Renew Energy 145:1597–1607

    Article  Google Scholar 

  74. Mahmood HA, Mariah Adam N, Sahari BB, Masuri SU (2018) Development of a particle swarm optimisation model for estimating the homogeneity of a mixture inside a newly designed CNG-H2-AIR mixer for a dual fuel engine: an experimental and theoretic study. Fuel 217:131–150

    Article  Google Scholar 

  75. Zhao R, Zhang H, Song S, Yang F, Hou X, Yang Y (2018) Global optimization of the diesel engine–organic Rankine cycle (ORC) combined system based on particle swarm optimizer (PSO). Energy Convers Manage 174:248–259

    Article  Google Scholar 

  76. Zhu H, Hu YM, Zhu WD, Fan W, Zhou BW (2020) Multi-objective design optimization of an engine accessory drive system with a robustness analysis. Appl Math Model 77:1564–1581

    Article  MathSciNet  MATH  Google Scholar 

  77. Liu J, Wang J, Zhao H (2018) Optimization of the injection parameters and combustion chamber geometries of a diesel/natural gas RCCI engine. Energy 164:837–852

    Article  Google Scholar 

  78. Simon VV (2020) Multi-objective optimization of hypoid gears to improve operating characteristics. Mech Mach Theory 146:103727

    Article  Google Scholar 

  79. Parmar A, Ramkumar P, Shankar K (2020) Macro geometry multi-objective optimization of planetary gearbox considering scuffing constraint. Mech Mach Theory 154:104045

    Article  Google Scholar 

  80. Donghui L, Zhenwei F, Zhang Y, Jian Z, Fengtian Y (2020) Optimum design and experiment of composite leaf spring landing gear for electric aircraft. Chin J Aeronaut 33(10):2649–2659

    Article  Google Scholar 

  81. Fei C-W, Li H, Liu H-T, Lu C, Keshtegar B, An L-Q (2020) Multilevel nested reliability-based design optimization with hybrid intelligent regression for operating assembly relationship. Aerosp Sci Technol 103:105906

    Article  Google Scholar 

  82. Yalcin Y, Orhon M, Pekcan O (2019) An automated approach for the design of Mechanically Stabilized Earth Walls incorporating metaheuristic optimization algorithms. Appl Soft Comput 74:547–566

    Article  Google Scholar 

  83. Cheng Z, Lu Z, Qian J (2019) A new non-geometric transmission parameter optimization design method for HMCVT based on improved GA and maximum transmission efficiency. Comput Electron Agric 167:105034

    Article  Google Scholar 

  84. Song CY, Lee J, Choi HY (2020) Multi-objective optimization in the vibration characteristics of a hydraulic steering system using a conservative and feasible response surface method. Eng Optim 52:465–483

    Article  Google Scholar 

  85. Rai P, Agrawal A, Saini ML, Jodder C, Barman AG (2018) Volume optimization of helical gear with profile shift using real coded genetic algorithm. Proc Comput Sci 133:718–724

    Article  Google Scholar 

  86. Peng M, Lin J, Liu X (2018) Optimizing design of powertrain transmission ratio of heavy duty truck. IFAC-PapersOnLine 51:892–897

    Article  Google Scholar 

  87. Robison A, Vacca A (2018) Multi-objective optimization of circular-toothed gerotors for kinematics and wear by genetic algorithm. Mech Mach Theory 128:150–168

    Article  Google Scholar 

  88. Zhang J, Qin X, Xie C, Chen H, Jin L (2018) Optimization design on dynamic load sharing performance for an in-wheel motor speed reducer based on genetic algorithm. Mech Mach Theory 122:132–147

    Article  Google Scholar 

  89. Eckert JJ, Santiciolli FM, Bertoti E, Costa ES, Corrêa FC, Silva LCAE et al (2018) Gear shifting multi-objective optimization to improve vehicle performance, fuel consumption, and engine emissions. Mech Des Struct Mach 46:238–253

    Article  Google Scholar 

  90. Wang C, Wang S, Wang G (2019) Volume models for different structures of spur gear. Aust J Mech Eng 17:145–153

    Article  Google Scholar 

  91. Zhang J-Y, Cai S-J, Li Y-J, Zhou X, Zhang Y-X (2017) Optimization design of multiphase pump impeller based on combined genetic algorithm and boundary vortex flux diagnosis. J Hydrodyn Ser B 29:1023–1034

    Article  Google Scholar 

  92. Eckert JJ, Corrêa FC, Santiciolli FM, Costa ES, Dionísio HJ, Dedini FG (2016) Vehicle gear shifting strategy optimization with respect to performance and fuel consumption. Mech Des Struct Mach 44:123–136

    Article  Google Scholar 

  93. Yu W, Li B, Jia H, Zhang M, Wang D (2015) Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy Build 88:135–143

    Article  Google Scholar 

  94. Castillo O, Cervantes L (2014) Genetic design of optimal type-1 and type-2 fuzzy systems for longitudinal control of an airplane. Intell Autom Soft Comput 20:213–227

    Article  Google Scholar 

  95. Karathanassis IK, Papanicolaou E, Belessiotis V, Bergeles GC (2013) Multi-objective design optimization of a micro heat sink for concentrating photovoltaic/thermal (CPVT) systems using a genetic algorithm. Appl Therm Eng 59:733–744

    Article  Google Scholar 

  96. Yildiz AR (2013) Comparison of evolutionary-based optimization algorithms for structural design optimization. Eng Appl Artif Intell 26:327–333

    Article  Google Scholar 

  97. Shi X (2011) Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy 36:1659–1667

    Article  Google Scholar 

  98. Godwin Raja Ebenezer N, Ramabalan S, Navaneethasanthakumar S (2020) Design optimisation of mating helical gears with profile shift using nature inspired algorithms. Aust J Mech Eng 1–8

  99. Wang C, Koh JM, Yu T, Xie NG, Cheong KH (2020) Material and shape optimization of bi-directional functionally graded plates by GIGA and an improved multi-objective particle swarm optimization algorithm. Comput Methods Appl Mech Eng 366:113017

    Article  MathSciNet  MATH  Google Scholar 

  100. Haidong S, Ziyang D, Junsheng C, Hongkai J (2020) Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO. ISA Trans 105:308–319

    Article  Google Scholar 

  101. Kar D, Ghosh M, Guha R, Sarkar R, Garcia-Hernandez L, Abraham A (2020) Fuzzy mutation embedded hybrids of gravitational search and particle swarm optimization methods for engineering design problems. Eng Appl Artif Intell 95:103847

    Article  Google Scholar 

  102. Gu J, Zhao Z, Chen Y, He L, Zhan X (2020) Integrated optimal design of configuration and parameter of multimode hybrid powertrain system with two planetary gears. Mech Mach Theory 143:103630

    Article  Google Scholar 

  103. Chen S-Y, Wu C-H, Hung Y-H, Chung C-T (2018) Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization. Energy 160:154–170

    Article  Google Scholar 

  104. Atila Ü, Dörterler M, Durgut R, Şahin İ (2020) A comprehensive investigation into the performance of optimization methods in spur gear design. Eng Optim 52:1052–1067

    Article  Google Scholar 

  105. Lebaal N (2019) Robust low cost meta-modeling optimization algorithm based on meta-heuristic and knowledge databases approach: Application to polymer extrusion die design. Finite Elem Anal Des 162:51–66

    Article  MathSciNet  Google Scholar 

  106. Zhang B, Song B, Mao Z, Li B (2018) Layout optimization of landing gears for an underwater glider based on particle swarm algorithm. Appl Ocean Res 70:22–31

    Article  Google Scholar 

  107. Sun S, Wang S, Wang Y, Lim TC, Yang Y (2018) Prediction and optimization of hobbing gear geometric deviations. Mech Mach Theory 120:288–301

    Article  Google Scholar 

  108. Wu Q, Cole C, McSweeney T (2016) Applications of particle swarm optimization in the railway domain. Int J Rail Transp 4:167–190

    Article  Google Scholar 

  109. Carbonelli A, Rigaud E, Perret-Liaudet J, Pelloli E, Barday D (2014) Low noise design of a truck timing multi-stage gear: robust optimization of tooth surface modifications. In: Lyon P, Velex Ed (eds) International Gear Conference 2014: 26th–28th August 2014. Chandos Publishing, Oxford, pp 200–207

    Google Scholar 

  110. Kaveh A, Biabani Hamedani K, Milad Hosseini S, Bakhshpoori T (2020) Optimal design of planar steel frame structures utilizing meta-heuristic optimization algorithms. Structures 25:335–346

    Article  Google Scholar 

  111. Li K, Yu Y, Wang Y, Hu Z (2018) Research on structural optimization method of FRP fishing vessel based on artificial bee colony algorithm. Adv Eng Softw 121:250–261

    Article  Google Scholar 

  112. Fang J, Sun G, Qiu N, Steven GP, Li Q (2017) Topology optimization of multicell tubes under out-of-plane crushing using a modified artificial bee colony algorithm. ASME. J Mech Des 139(7):071403

    Article  Google Scholar 

  113. Aydoğdu İ, Akın A, Saka MP (2016) Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Adv Eng Softw 92:1–14

    Article  Google Scholar 

  114. Liang J-H, Lee C-H (2015) A Modification artificial bee colony algorithm for optimization problems. Math Probl Eng 2015:581391

    Article  Google Scholar 

  115. Garg H (2014) Solving structural engineering design optimization problems using an artificial bee colony algorithm. J Ind Manag Optim 10:777–794

    Article  MathSciNet  MATH  Google Scholar 

  116. Jahjouh MM, Arafa MH, Alqedra MA (2013) Artificial Bee Colony (ABC) algorithm in the design optimization of RC continuous beams. Struct Multidiscip Optim 47:963–979

    Article  Google Scholar 

  117. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23:1001–1014

    Article  Google Scholar 

  118. Shilaja C, Arunprasath T (2019) Optimal power flow using moth swarm algorithm with gravitational search algorithm considering wind power. Futur Gener Comput Syst 98:708–715

    Article  Google Scholar 

  119. Gupta S, Deep K (2020) A memory-based Grey Wolf Optimizer for global optimization tasks. Appl Soft Comput 93:106367

    Article  Google Scholar 

  120. Tripathi S, Shrivastava A, Jana KC (2020) Self-Tuning fuzzy controller for sun-tracker system using Gray Wolf Optimization (GWO) technique. ISA Trans 101:50–59

    Article  Google Scholar 

  121. Dörterler M, Şahin İ, Gökçe H (2019) A grey wolf optimizer approach for optimal weight design problem of the spur gear. Eng Optim 51:1013–1027

    Article  Google Scholar 

  122. Nayak B, Misra B, Choudhury TR (2018) Meta-heuristic optimization algorithms for design of gain constrained state variable filter. AEU-Int J Electron C 93:7–18

    Article  Google Scholar 

  123. Chai J, Huang P, Sun Y (2020) Differential evolution-based system design optimization for net zero energy buildings under climate change. Sustain Urban Areas 55:102037

    Google Scholar 

  124. Truong V-H, Kim S-E (2018) Reliability-based design optimization of nonlinear inelastic trusses using improved differential evolution algorithm. Adv Eng Softw 121:59–74

    Article  Google Scholar 

  125. de Vasconcelos Segundo EH, Amoroso AL, Mariani VC, dos Santos Coelho L (2017) Economic optimization design for shell-and-tube heat exchangers by a Tsallis differential evolution. Appl Therm Eng 111:143–151

    Article  Google Scholar 

  126. Ho-Huu V, Nguyen-Thoi T, Le-Anh L, Nguyen-Trang T (2016) An effective reliability-based improved constrained differential evolution for reliability-based design optimization of truss structures. Adv Eng Softw 92:48–56

    Article  Google Scholar 

  127. Pati PR, Satpathy MP (2019) Investigation on red brick dust filled epoxy composites using ant lion optimization approach. Polym Compos 40:3877–3885

    Article  Google Scholar 

  128. Coelho LS, Maidl G, Pierezan J, Mariani VC, Luz MVF, Leite JV (2018) Ant Lion approach based on Lozi Map for multiobjective transformer design optimization. In: 2018 international symposium on power electronics, electrical drives, automation and motion (SPEEDAM), pp 280–285

  129. Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46:79–95

    Article  Google Scholar 

  130. Dai C, Lei X, He X (2020) A decomposition-based evolutionary algorithm with adaptive weight adjustment for many-objective problems. Soft Comput 24:10597–10609

    Article  Google Scholar 

  131. Han D, Du W, Du W, Jin Y, Wu C (2019) An adaptive decomposition-based evolutionary algorithm for many-objective optimization. Inf Sci 491:204–222

    Article  MathSciNet  MATH  Google Scholar 

  132. Tanabe R, Ishibuchi H (2018) A decomposition-based evolutionary algorithm for multi-modal multi-objective optimization. In: International conference on parallel problem solving from nature, pp 249–261

  133. Shankar Bhattacharjee K, Kumar Singh H, Ray T (2017) A novel decomposition-based evolutionary algorithm for engineering design optimization. ASME. J Mech Des 139(4):041403

    Article  Google Scholar 

  134. Got A, Moussaoui A, Zouache D (2020) A guided population archive whale optimization algorithm for solving multiobjective optimization problems. Expert Syst Appl 141:112972

    Article  Google Scholar 

  135. Panda S, Mishra D, Biswal B (2019) An approach for design optimization of 3R manipulator using Adaptive Cuckoo Search algorithm. Mech Des Struct Mach 48(6):1–26

    Google Scholar 

  136. Du T-S, Ke X-T, Liao J-G, Shen Y-J (2018) DSLC-FOA: improved fruit fly optimization algorithm for application to structural engineering design optimization problems. Appl Math Model 55:314–339

    Article  MathSciNet  MATH  Google Scholar 

  137. Pauline O, Sin HC, Sheng DDCV, Kiong SC, Meng OK (2017) Design optimization of structural engineering problems using adaptive cuckoo search algorithm. In: 2017 3rd international conference on control, automation and robotics (ICCAR), pp 745–748

  138. Kaur S, Awasthi LK, Sangal AL, Dhiman G (2020) Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell 90:103541

    Article  Google Scholar 

  139. Gupta S, Deep K, Mirjalili S (2020) An efficient equilibrium optimizer with mutation strategy for numerical optimization. Appl Soft Comput 96:106542

    Article  Google Scholar 

  140. Talatahari S, Azizi M (2020) Optimization of constrained mathematical and engineering design problems using chaos game optimization. Comput Ind Eng 145:106560

    Article  Google Scholar 

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Acknowledgments

This work is supported by the Hubei Provincinal Science and Technology Major Project of China under Grant No. 2020AEA011 and the Key Research & Developement Plan of Hubei Province of China under Grant No. 2020BAB100. Also, China Postdoctoral Science Foundation Grant No. 2019M652647.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed Abd Elaziz

  2. Production Engineering and Mechanical Design Department, Faculty of Engineering, Tanta University, Tanta, Egypt

    Ammar H. Elsheikh

  3. Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico

    Diego Oliva

  4. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan

    Laith Abualigah

  5. Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Songfeng Lu

  6. Department of Computer, Damietta University, Damietta, 34517, Egypt

    Ahmed A. Ewees

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  1. Mohamed Abd Elaziz
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  2. Ammar H. Elsheikh
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  3. Diego Oliva
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  4. Laith Abualigah
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  5. Songfeng Lu
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  6. Ahmed A. Ewees
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Correspondence to Mohamed Abd Elaziz.

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Abd Elaziz, M., Elsheikh, A.H., Oliva, D. et al. Advanced Metaheuristic Techniques for Mechanical Design Problems: Review. Arch Computat Methods Eng 29, 695–716 (2022). https://doi.org/10.1007/s11831-021-09589-4

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