Skip to main content
SpringerLink
Log in

Swarm Intelligence-Based Methods

  • Chapter
  • First Online:
Computational Methods for Application in Industry 4.0

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSMANUFACT))

Abstract

The term “Swarm Intelligence” refers directly to the collective behavior of a group of animals, which are following very basic rules, or to an Artificial Intelligence approach, which aims at the solution of a problem using algorithms based on collective behavior of social animals. For over three decades, several algorithms based on the observation of the behavior of groups of animals were developed, such as Particle Swarm Optimization, from the observation of flocks of birds. Some of the most established Swarm Intelligence (SI) methods include the Ant Colony Optimization method, the Harmony Search method and the Artificial Bee Colony algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sörensen K (2015) Metaheuristics—the metaphor exposed. Int Trans Oper Res 22:3–18

    Article  MathSciNet  Google Scholar 

  2. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, WA, Australia, pp 1942–1948

    Google Scholar 

  3. Khare A, Rangnekar S (2013) A review of particle swarm optimization and its applications in solar photovoltaic system. Appl Soft Comput 13:2997–3006

    Article  Google Scholar 

  4. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1:33–57

    Article  Google Scholar 

  5. Fatih Tasgetiren M, Liang Y-C, Sevkli M, Gencyilmaz G (2006) Particle swarm optimization and differential evolution for the single machine total weighted tardiness problem. Int J Prod Res 44:4737–4754

    Article  Google Scholar 

  6. Guo YW, Li WD, Mileham AR, Owen GW (2009) Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach. Int J Prod Res 47:3775–3796

    Article  Google Scholar 

  7. Tsai C-Y, Kao I-W (2011) Particle swarm optimization with selective particle regeneration for data clustering. Expert Syst Appl 38:6565–6576

    Article  Google Scholar 

  8. Esmin AAA, Coelho RA, Matwin S (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif Intell Rev 44:23–45

    Article  Google Scholar 

  9. Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. J Math Probl Eng 931256

    Google Scholar 

  10. Wang YF, Zhang YF, Fuh JYH (2012) A hybrid particle swarm based method for process planning optimisation. Int J Prod Res 50:277–292

    Article  Google Scholar 

  11. Samarghandi H, ElMekkawy TY (2014) Solving the no-wait flow-shop problem with sequence-dependent set-up times. Int J Comput Integr Manuf 27:213–228

    Article  Google Scholar 

  12. Attar SF, Mohammadi M, Tavakkoli-Moghaddam R, Yaghoubi S (2014) Solving a new multi-objective hybrid flexible flowshop problem with limited waiting times and machine-sequence-dependent set-up time constraints. Int J Comput Integr Manuf 27:450–469

    Article  Google Scholar 

  13. Che ZH (2017) A multi-objective optimization algorithm for solving the supplier selection problem with assembly sequence planning and assembly line balancing. Comput Ind Eng 105:247–259

    Article  Google Scholar 

  14. Keshtzari M, Naderi B, Mehdizadeh E (2016) An improved mathematical model and a hybrid metaheuristic for truck scheduling in cross-dock problems. Comput Ind Eng 91:197–204

    Article  Google Scholar 

  15. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Techical report tr-06, Erciyes Engineering Faculty, Kayseri

    Google Scholar 

  16. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132

    MathSciNet  MATH  Google Scholar 

  17. Dunder E, Gumustekin S, Cengiz MA (2018) Variable selection in gamma regression models via artificial bee colony algorithm. J Appl Stat 45:8–16

    Article  MathSciNet  Google Scholar 

  18. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697

    Article  Google Scholar 

  19. Bulut O, Tasgetiren MF (2014) An artificial bee colony algorithm for the economic lot scheduling problem. Int J Prod Res 52:1150–1170

    Article  Google Scholar 

  20. Li X, Yang G (2016) Artificial bee colony algorithm with memory. Appl Soft Comput 41:362–372

    Article  Google Scholar 

  21. Hemamalini S, Simon SP (2010) Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions. Electr Power Components Syst 38:786–803

    Article  Google Scholar 

  22. Lei D, Guo X (2013) Scheduling job shop with lot streaming and transportation through a modified artificial bee colony. Int J Prod Res 51:4930–4941

    Article  MathSciNet  Google Scholar 

  23. Ng KKH, Lee CKM, Zhang SZ, Wu K, Ho W (2017) A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion. Comput Ind Eng 109:151–168

    Article  Google Scholar 

  24. Yazdani M, Gohari S, Naderi B (2015) Multi-factory parallel machine problems: improved mathematical models and artificial bee colony algorithm. Comput Ind Eng 81:36–45

    Article  Google Scholar 

  25. Wang X, Xie X, Cheng TCE (2013) A modified artificial bee colony algorithm for order acceptance in two-machine flow shops. Int J Prod Econ 141:14–23

    Article  Google Scholar 

  26. Zhang R, Song S, Wu C (2013) A hybrid artificial bee colony algorithm for the job shop scheduling problem. Int J Prod Econ 141:167–178

    Article  Google Scholar 

  27. Dorigo M (1992) Optimization, LEARNING AND NATURAL ALGorithms (in Italian). Dipartimento di Elettronica, Politecnico di Milano

    Google Scholar 

  28. Osman H, Baki MF (2014) Balancing transfer lines using benders decomposition and ant colony optimisation techniques. Int J Prod Res 52:1334–1350

    Article  Google Scholar 

  29. Adubi SA, Misra S (2014) A comparative study on the ant colony optimization algorithms. In: 2014 11th international conference on electronics, computer and computation (ICECCO), Abuja, Nigeria, pp 1–4

    Google Scholar 

  30. Shyu SJ, Yin PY, Lin BMT, Haouari M (2003) Ant-tree: an ant colony optimization approach to the generalized minimum spanning tree problem. J Exp Theor Artif Intell 15:103–112

    Article  Google Scholar 

  31. Cordon O, Herrera F, Stützle T (2003) A review on the ant colony optimization metaheuristic: basis, models and new trends. Mathware Soft Comput 9 (2–3)

    Google Scholar 

  32. Zecchin AC, Simpson AR, Maier HR, Leonard M, Roberts AJ, Berrisford MJ (2006) Application of two ant colony optimisation algorithms to water distribution system optimisation. Math Comput Model 44:451–468

    Article  Google Scholar 

  33. Wong TN, Zhang S, Wang G, Zhang L (2012) Integrated process planning and scheduling—multi-agent system with two-stage ant colony optimisation algorithm. Int J Prod Res 50:6188–6201

    Article  Google Scholar 

  34. Maniezzo V, Gambardella LM, de Luigi F (2004) Ant colony optimization. In: Onwubolu GC, Babu BV (eds) New optimization techniques in engineering. Springer, Berlin, pp 101–121

    Chapter  Google Scholar 

  35. Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2:353–373

    Article  Google Scholar 

  36. Yakıcı E (2017) A heuristic approach for solving a rich min-max vehicle routing problem with mixed fleet and mixed demand. Comput Ind Eng 109:288–294

    Article  Google Scholar 

  37. Seo M, Kim D (2010) Ant colony optimisation with parameterised search space for the job shop scheduling problem. Int J Prod Res 48:1143–1154

    Article  Google Scholar 

  38. Huang R-H (2010) Multi-objective job-shop scheduling with lot-splitting production. Int J Prod Econ 124:206–213

    Article  Google Scholar 

  39. Chen H, Du B, Huang GQ (2010) Metaheuristics to minimise makespan on parallel batch processing machines with dynamic job arrivals. Int J Comput Integr Manuf 23:942–956

    Article  Google Scholar 

  40. Yagmahan B, Yenisey MM (2008) Ant colony optimization for multi-objective flow shop scheduling problem. Comput Ind Eng 54:411–420

    Article  Google Scholar 

  41. Moncayo-Martínez LA, Zhang DZ (2013) Optimising safety stock placement and lead time in an assembly supply chain using bi-objective MAX–MIN ant system. Int J Prod Econ 145:18–28

    Article  Google Scholar 

  42. Hosseini HS (2007) Problem solving by intelligent water drops. In: 2007 IEEE congress on evolutionary computation, Singapore, Singapore, pp 3226–3231

    Google Scholar 

  43. Niu SH, Ong SK, Nee AYC (2012) An improved intelligent water drops algorithm for achieving optimal job-shop scheduling solutions. Int J Prod Res 50:4192–4205

    Article  Google Scholar 

  44. Hosseini HS (2009) The intelligent water drops algorithm, a nature inspired swarm based optimization algorithm. J Int J Bio-Inspired Comput 1:71–79

    Article  Google Scholar 

  45. Alijla BO, Wong L-P, Lim CP, Khader AT, Al-Betar MA (2014) A modified intelligent water drops algorithm and its application to optimization problems. Expert Syst Appl 41:6555–6569

    Article  Google Scholar 

  46. Niu SH, Ong SK, Nee AYC (2013) An improved intelligent water drops algorithm for solving multi-objective job shop scheduling. Eng Appl Artif Intell 26:2431–2442

    Article  Google Scholar 

  47. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68

    Article  Google Scholar 

  48. Yang X-S (2009) Harmony search as a metaheuristic algorithm. In: Geem ZW (ed) Music-inspired harmony search algorithm: theory and applications. Springer, Berlin, pp 1–14

    Google Scholar 

  49. Wang X, Gao X-Z, Zenger K (2015) The overview of harmony search. In: Wang X, Gao X-Z, Zenger K (eds) An introduction to harmony search optimization method. Springer International Publishing, Cham, pp 5–11

    Google Scholar 

  50. Gao XZ, Govindasamy V, Xu H, Wang X, Zenger K (2015) Harmony search method: theory and applications. Comput Intell Neurosci 258491

    Google Scholar 

  51. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579

    MathSciNet  MATH  Google Scholar 

  52. Manjarres D, Landa-Torres I, Gil-Lopez S, Del Ser J, Bilbao MN, Salcedo-Sanz S, Geem ZW (2013) A survey on applications of the harmony search algorithm. Eng Appl Artif Intell 26:1818–1831

    Article  Google Scholar 

  53. Purnomo HD, Wee H-M (2014) Maximizing production rate and workload balancing in a two-sided assembly line using harmony search. Comput Ind Eng 76:222–230

    Article  Google Scholar 

  54. Zammori F, Braglia M, Castellano D (2014) Harmony search algorithm for single-machine scheduling problem with planned maintenance. Comput Ind Eng 76:333–346

    Article  Google Scholar 

  55. Alaei S, Setak M (2015) Multi objective coordination of a supply chain with routing and service level consideration. Int J Prod Econ 167:271–281

    Article  Google Scholar 

  56. Wong WK, Guo ZX (2010) A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. Int J Prod Econ 128:614–624

    Article  Google Scholar 

  57. Vahedi Nouri B, Fattahi P, Ramezanian R (2013) Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities. Int J Prod Res 51:3501–3515

    Article  Google Scholar 

  58. Rohaninejad M, Kheirkhah AS, Vahedi Nouri B, Fattahi P (2015) Two hybrid tabu search–firefly algorithms for the capacitated job shop scheduling problem with sequence-dependent setup cost. Int J Comput Integr Manuf 28:470–487

    Article  Google Scholar 

  59. Yang X-S (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications. Springer, Berlin, pp 169–178

    Chapter  Google Scholar 

  60. Sayadi MK, Hafezalkotob A, Naini SGJ (2013) Firefly-inspired algorithm for discrete optimization problems: an application to manufacturing cell formation. J Manuf Syst 32:78–84

    Article  Google Scholar 

  61. Yu S, Su S, Lu Q, Huang L (2014) A novel wise step strategy for firefly algorithm. Int J Comput Math 91:2507–2513

    Article  MathSciNet  Google Scholar 

  62. Fister I, Fister I, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46

    Article  Google Scholar 

  63. Hackl A, Magele C, Renhart W (2016) Extended firefly algorithm for multimodal optimization. In: 2016 19th international symposium on electrical apparatus and technologies (SIELA), Bourgas, Bulgaria, pp 1–4

    Google Scholar 

  64. Gandomi AH, Yang X-S, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18:89–98

    Article  MathSciNet  Google Scholar 

  65. Alinaghian M, Naderipour M (2016) A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: a case study. Comput Ind Eng 99:210–222

    Article  Google Scholar 

  66. Madani-Isfahani M, Tavakkoli-Moghaddam R, Naderi B (2014) Multiple cross-docks scheduling using two meta-heuristic algorithms. Comput Ind Eng 74:129–138

    Article  Google Scholar 

  67. Yang XS, Deb S (2009) Cuckoo search via levy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC), Coimbatore, India, pp 210–214

    Google Scholar 

  68. Yildiz AR (2013) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64:55–61

    Article  Google Scholar 

  69. Bulatović RR, Đorđević SR, Đorđević VS (2013) Cuckoo search algorithm: a metaheuristic approach to solving the problem of optimum synthesis of a six-bar double dwell linkage. Mech Mach Theory 61:1–13

    Article  Google Scholar 

  70. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35

    Article  Google Scholar 

  71. Mohamad AB, Zain AM, Nazira Bazin NE (2014) Cuckoo search algorithm for optimization problems—a literature review and its applications. Appl Artif Intell 28:419–448

    Article  Google Scholar 

  72. Valian E, Tavakoli S, Mohanna S, Haghi A (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64:459–468

    Article  Google Scholar 

  73. Kanagaraj G, Ponnambalam SG, Jawahar N (2013) A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems. Comput Ind Eng 66:1115–1124

    Article  Google Scholar 

  74. Pan W-T (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74

    Article  Google Scholar 

  75. Xing B, Gao W-J (2016) Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer International Publishing, Switzerland

    MATH  Google Scholar 

  76. Zheng X, Wang L (2016) A knowledge-guided fruit fly optimization algorithm for dual resource constrained flexible job-shop scheduling problem. Int J Prod Res 54:5554–5566

    Article  Google Scholar 

  77. Mitić M, Vuković N, Petrović M, Miljković Z (2015) Chaotic fruit fly optimization algorithm. Knowl-Based Syst 89:446–458

    Article  Google Scholar 

  78. Han Y, Gong D, Li J, Zhang Y (2016) Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm. Int J Prod Res 54:6782–6797

    Article  Google Scholar 

  79. Oftadeh R, Mahjoob MJ (2009) A new meta-heuristic optimization algorithm: hunting search. In: 2009 fifth international conference on soft computing, computing with words and perceptions in system analysis, decision and control, Famagusta, Cyprus, pp 1–5

    Google Scholar 

  80. Oftadeh R, Mahjoob MJ, Shariatpanahi M (2010) A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput Math with Appl 60:2087–2098

    Article  Google Scholar 

  81. Yazdani M, Naderi B, Mousakhani M (2015) a model and metaheuristic for truck scheduling in multi-door cross-dock problems. Intell Autom Soft Comput 21:633–644

    Article  Google Scholar 

  82. Bouzaida S, Sakly A, M’Sahli F (2014) Extracting TSK-type neuro-fuzzy model using the hunting search algorithm. Int J Gen Syst 43:32–43

    Article  MathSciNet  Google Scholar 

  83. Zare K, Hashemi SM (2012) A solution to transmission-constrained unit commitment using hunting search algorithm. In: 2012 11th international conference on environment and electrical engineering, Venice, Italy, pp 941–946

    Google Scholar 

  84. Naderi B, Khalili M, Khamseh AA (2014) Mathematical models and a hunting search algorithm for the no-wait flowshop scheduling with parallel machines. Int J Prod Res 52:2667–2681

    Article  Google Scholar 

  85. Dogan E (2014) Solving design optimization problems via hunting search algorithm with Levy flights. Struct Eng Mech 52(2):351–358

    Article  Google Scholar 

  86. Duman E, Uysal M, Alkaya AF (2012) Migrating birds optimization: a new metaheuristic approach and its performance on quadratic assignment problem. Inf Sci 217:65–77

    Article  MathSciNet  Google Scholar 

  87. Tongur V, Ülker E (2016) The analysis of migrating birds optimization algorithm with neighborhood operator on traveling salesman problem. In: Lavangnananda K, Phon-Amnuaisuk S, Engchuan W, Chan JH (eds) Intelligent and evolutionary systems. Springer International Publishing, Cham, pp 227–237

    Chapter  Google Scholar 

  88. Tongur V, Erkan Ü (2014) Migrating birds optimization for flow shop sequencing problem. J Comput Commun 2:142

    Article  Google Scholar 

  89. Gao KZ, Suganthan PN, Chua TJ (2013) An enhanced migrating birds optimization algorithm for no-wait flow shop scheduling problem. In: 2013 IEEE symposium on computational intelligence in scheduling (CISched), Singapore, Singapore, pp 9–13

    Google Scholar 

  90. Soto R, Crawford B, Almonacid B, Paredes F (2016) Efficient parallel sorting for migrating birds optimization when solving machine-part cell formation problems. Sci Program 9402503

    Google Scholar 

  91. Alkaya AF, Algin R, Sahin Y, Agaoglu M, Aksakalli V (2014) Performance of migrating birds optimization algorithm on continuous functions. In: Tan Y, Shi Y, Coello CAC (eds) Advances in swarm intelligence. Springer International Publishing, Cham, pp 452–459

    Google Scholar 

  92. Benkalai I, Rebaine D, Gagné C, Baptiste P (2017) Improving the migrating birds optimization metaheuristic for the permutation flow shop with sequence-dependent set-up times. Int J Prod Res 55:6145–6157

    Article  Google Scholar 

  93. Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation. Springer, Berlin, pp 240–249

    Chapter  Google Scholar 

  94. Zhang M, Pratap S, Huang GQ, Zhao Z (2017) Optimal collaborative transportation service trading in B2B e-commerce logistics. Int J Prod Res 55:5485–5501

    Article  Google Scholar 

  95. Ibanez S (2012) Optimizing size thresholds in a plant-pollinator interaction web: towards a mechanistic understanding of ecological networks. Oecologia 170:233–242

    Article  Google Scholar 

  96. Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192–203

    Article  Google Scholar 

  97. Abdelaziz AY, Ali ES (2015) Static VAR compensator damping controller design based on flower pollination algorithm for a multi-machine power system. Electr Power Compon Syst 43:1268–1277

    Article  Google Scholar 

  98. He X, Yang X-S, Karamanoglu M, Zhao Y (2017) Global convergence analysis of the flower pollination algorithm: a discrete-time Markov chain approach. Procedia Comput Sci 108:1354–1363

    Article  Google Scholar 

  99. Bozorgi A, Bozorg-Haddad O, Chu X (2018) Anarchic society optimization (ASO) algorithm. In: Bozorg-Haddad O (ed) Advanced optimization by nature-inspired algorithms. Springer Singapore, Singapore, pp 31–38

    Google Scholar 

  100. Ahmadi-Javid A (2011) Anarchic society optimization: a human-inspired method. In: 2011 IEEE congress of evolutionary computation (CEC), New Orleans, LA, pp 2586–2592

    Google Scholar 

  101. Ahmadi-Javid A, Hooshangi-Tabrizi P (2015) A mathematical formulation and anarchic society optimisation algorithms for integrated scheduling of processing and transportation operations in a flow-shop environment. Int J Prod Res 53:5988–6006

    Article  Google Scholar 

  102. Bozorg-Haddad O, Latifi M, Bozorgi A, Rajabi M-M, Naeeni S-T, Loáiciga HA (2018) Development and application of the anarchic society algorithm (ASO) to the optimal operation of water distribution networks. Water Sci Technol Water Supply 18:318–332

    Article  Google Scholar 

  103. Shayeghi H (2012) Anarchic society optimization based pid control of an automatic voltage regulator (AVR) system. Electr Electron Eng 2(4):199–207

    Article  Google Scholar 

  104. Ahmadi-Javid A, Hooshangi-Tabrizi P (2017) Integrating employee timetabling with scheduling of machines and transporters in a job-shop environment: a mathematical formulation and an anarchic society optimization algorithm. Comput Oper Res 84:73–91

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Manufacturing Technology, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece

    Nikolaos E. Karkalos & Angelos P. Markopoulos

  2. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

Authors
  1. Nikolaos E. Karkalos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angelos P. Markopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Paulo Davim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikolaos E. Karkalos .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s)

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Karkalos, N.E., Markopoulos, A.P., Davim, J.P. (2019). Swarm Intelligence-Based Methods. In: Computational Methods for Application in Industry 4.0. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-92393-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-92393-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92392-5

  • Online ISBN: 978-3-319-92393-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation