Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

Abstract

The current state of the art of materials research using multi-objective genetic and evolutionary algorithms is briefly presented with critical analyses. The basic concepts of multi-objective optimisation and Pareto optimality are explained in simple terms and the advantages of an evolutionary approach are emphasised. Current materials related research in this area is summarised, focusing on the achievements to date and the specific needs for further improvement.

Keywords

Genetic algorithms Multi-objective optimisation Materials design and processing Reviews Critical assessments

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References

Miettinen

: ‘Nonlinear multiobjective optimization’; 1999, Boston, MA, Kluwer Academic Publishers.

Coello

CAC

, Lamont

and Van Veldhuisen

: ‘Evolutionary algorithms for solving multi-objective problems’, 2nd edn; 2007, New York, Springer.

Deb

: ‘Multi-objective optimization using evolutionary algorithms’; 2001, Chichester, John Wiley & Sons.

Nishimoto

, Saida

and Shinohara

: ‘Computer aided alloy design of insert metal for transient liquid phase bonding of γ/γ′/β type high aluminium nickel base superalloy’, Sci. Technol. Weld. Joining , 2003, 8, 29–38.

Tancret

: ‘Computational thermodynamics, Gaussian processes and genetic algorithms: combined tools to design new alloys’, Modelling Simul. Mater. Sci. Eng., 2013, 21, 045013, DOI: 10.1088/0965-0393/21/4/045013.

Tancret

: ‘Computational thermodynamics and genetic algorithms to design affordable gamma′-strengthened nickel-iron based superalloys’, Modelling Simul. Mater. Sci. Eng., 2012, 20, 045012, DOI: 10.1088/0965-0393/20/4/045012.

Pettersson

, Chakraborti

and Saxén

: ‘A genetic algorithms based multi-objective neural net applied to noisy blast furnace data’, Appl. Soft Comput. , 2007, 7, 387–397.

Ghosh

, Singh

and Chakraborti

: ‘Optimization of stability of retained austenite in TRIP aided steel using data-driven models and multi-objective genetic algorithms’, J. ASTM Int. (Mater. Perform. Charact.) , 2012, 1.

Sun

, Zhao

, Zhang

, Guan Y

and Gao

: ‘Optimal design of second-step welding chamber for a condenser tube extrusion die based on the response surface method and the genetic algorithm’, Mater. Manuf. Process. , 2013, 28, 823–834.

10.

Chakraborti

: in ‘Pareto-optimality in design and manufacturing and how genetic algorithms handle it. Handbook of research on nature inspired computing for economy and management’; 2006, Hershey, PA, Idea Group Inc.

11.

Chakraborti

: ‘Promise of multiobjective genetic algorithms in coating performance formulation’, Surf. Eng. , 2014, 30, 79–82.

12.

Ganguly

, Kong

, Broderick SR and Rajan

: ‘Informatics-based uncertainty quantification in the design of inorganic scintillators’, Mater. Manuf. Process. , 2013, 28, 726–732.

13.

Kovačič

, Rožej

and Brezočnik

: ‘Genetic algorithm rolling mill layout optimization’, Mater. Manuf. Process. , 2013, 28, 783–787.

14.

Sharma

, Pant

and Singh

: ‘Nature-inspired metaheuristic techniques as powerful optimizers in the paper industry’, Mater. Manuf. Process. , 2013, 28, 788–802.

15.

Datta

and Chattopadhyay

: ‘Soft computing techniques in advancement of structural metals’, Int. Mater. Rev. , 2013, 58, 475–504.

16.

Paszkowicz

: ‘Genetic algorithms, a nature-inspired tool: a survey of applications in materials science and related fields. Part II’. Mater. Manuf. Process. , 2013, 28, 708–725.

17.

Mitra

: ‘Genetic algorithms in polymeric material production, design, processing and other applications: a review’, Int. Mater. Rev. , 2008, 53, 275–297.

18.

Chakraborti

: ‘Genetic algorithms in materials design and processing’, Int.Mater.Rev. , 2004, 49, 246–260.

19.

Chakraborti

: ‘Chapter 5 – Evolutionary data-driven modeling’, in ‘Informatics for materials science and engineering’, 71–95; 2013, Oxford, Butterworth-Heinemann.

20.

Coello-Coello

and Ricardo

: ‘Evolutionary multiobjective optimization in materials science and engineering’, Mater. Manuf. Process. , 2009, 24, 119–129.

21.

Jha

, Sen

and Chakraborti

: ‘Multi-objective genetic algorithms and genetic programming models for minimizing input carbon rates in a blast furnace compared with a conventional analytic approach’, Steel Res. Int. , 2014, 85, 219–232.

22.

Kumar

, Chakrabarti

and Chakraborti

: ‘Data-driven Pareto optimization for microalloyed steels using genetic algorithms’, Steel Res. Int. , 2012, 83, 169–174.

23.

Sinha

, Sikdar

, Chattopadhyay

and Datta

: ‘Optimization of mechanical property and shape recovery behavior of Ti–(similar to 49 at.) Ni alloy using artificial neural network and genetic algorithm’, Mater. Design , 2013, 46, 227–234.

24.

Datta

, Zhang

, Sultana

and Mahfouf

: ‘Optimal design of titanium alloys for prosthetic applications using a multiobjective evolutionary algorithm’, Mater. Manuf. Process. , 2013, 28, 741–745.

25.

Yegorov-Egorov

, Dulikravich

and Colaço

: ‘Optimizing chemistry of bulk metallic glasses for improved thermal stability’, Modelling Simul. Mater. Sci. Eng., 2008, 16, 075010, DOI: 10.1088/0965-0393/16/7/075010.

26.

Bansal

, Barman

, Ghosh

and Chakraborti

: ‘Designing Cu–Zr glass using multiobjective genetic algorithm and evolutionary neural network metamodels–based classical molecular dynamics simulation’, Mater. Manuf. Process. , 2013, 28, 733–740.

27.

Nandi

, Deb

, Ganguly

and Datta

: ‘ Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms’, Appl. Soft Comput. , 2012, 12, 28–39.

28.

Nandi

, Deb

and Datta

: ‘Genetic algorithm–based design and development of particle-reinforced silicone rubber for soft tooling process’, Mater. Manuf. Process. , 2013, 28, 753–760.

29.

Gujarathi

, Motagamwala

and Babu

: ‘Multiobjective optimization of industrial naphtha cracker for production of ethylene and propylene’, Mater. Manuf. Process. , 2013, 28, 803–810.

30.

Mitra

: ‘Assessing optimal growth of desired species in epoxy polymerization under uncertainty’, Chem. Eng. J. , 2010, 162, 322–330.

31.

Zhang

, Mahfouf

, Panoutsos

, Beamish

and Norris

: ‘Knowledge discovery for friction stir welding via data driven approaches. Part 2 – multiobjective modelling using fuzzy rule based systems’, Sci. Technol. Weld. Joining , 2012, 17, 681–693.

32.

Tutum

, Deb

and Hattel

: ‘Multi-criteria optimization in friction stir welding using a thermal model with prescribed material flow’, Mater. Manuf. Process. , 2013, 28, 816–822.

33.

Hariharan

, Nguyen

N.-T

, Chakraborti

, Lee

M.-G

and Barlat

: ‘Multi-objective genetic algorithm to optimize variable drawbead geometry for tailor welded blanks made of dissimilar steels’, Steel Res. Int., 2014, in press, DOI: 10.1002/srin.201300471.

34.

Ulutan

and Özel

, ‘Multiobjective optimization of experimental and simulated residual stresses in turning of nickel-alloy IN100’. Mater. Manuf. Process. , 2013, 28, 835–841.

35.

, Chen

Y.-Y

, Chen

C.-Y

, Shen

C.-H

, Cheng

H.-W

, Lo

I.-H

and Chen

C.-N

: ‘Device simulation–based multiobjective evolutionary algorithm for process optimization of semiconductor solar cells’, Mater. Manuf. Process. , 2013, 28, 761–767.

36.

Mitra

: ‘Evolutionary surrogate optimization of an industrial sintering process’, Mater. Manuf. Process. , 2013, 28, 768–775.

37.

Hariharan

, Chakraborti

, Lee

M.-G

and Barlat

: ‘A novel multi-objective genetic algorithms based calculation of Hill's coefficients’, Metall. Mater. Trans. A , 2014, 45, 2704–2707.

38.

Bhattacharya

, Kumar

GDR

, Agarwal

, Erkoç

, Singh

and Chakraborti

: ‘Analyzing Fe–Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms’, Comp. Mater. Sci. , 2009, 46, 821–827.

39.

Rajak

, Tewary

, Das

, Bhattacharya

and Chakraborti

: ‘Phases in the Zn-coated Fe analyzed through an evolutionary meta-model and multi-objective genetic algorithms’, Comp. Mater. Sci. , 2011, 50, 2502–2516.

40.

Rajak

, Ghosh

, Bhattacharya

and Chakraborti

: ‘Pareto-optimal analysis of Zn-coated Fe in the presence of dislocations using genetic algorithms’, Comp. Mater. Sci. , 2012, 62, 266–271.

41.

Bag

, De

and DebRoy

: ‘A genetic algorithm-assisted inverse convective heat transfer model for tailoring weld geometry’, Mater. Manuf. Process. , 2009, 24, 384–397.

42.

Giri

, Hakanen

, Miettinen

and Chakraborti

: ‘Genetic Programming through Bi-objective genetic algorithms with study of a sSimulated moving bed process involving multiple objectives’, Appl. Soft Comput., 2013, 13, 2613–2623.

43.

Giri

, Pettersson

, Saxén

and Chakraborti

: ‘Genetic programming evolved through bi-objective genetic algorithms applied to a blast furnace’, Mater. Manuf. Process. , 2013, 28, 776–782.

44.

Kant

et al.: ‘Comparison of multi-objective evolutionary neural network, adaptive neuro-fuzzy inference system and bootstrap-based neural network for flood forecasting’, Neural Comput. Appl. , 2013, 23, S231–S246.

45.

Hill

: ‘A theory of the yielding and plastic flow of anisotropic metals’. Proc. R. Soc. Lond. A: Math. Phys. Sci. , 1948, 193, (1033), 281–297.

46.

Nandan

, DebRoy

and Bhadeshia

HKDH

: ‘Recent advances in friction-stir welding – process, weldment structure and properties’, Prog. Mater. Sci. , 2008, 53, 980–1023.

47.

Kotas

and Hattel

: ‘Modelling and simulation of A segregates in steel castings using thermal criterion function. Part II – Optimisation of real industrial cast part’, Mater. Sci. Technol. , 2012, 28, 911–917.

48.

Mondal

, Sarangi

, Pettersson

, Sen

, Saxén

and Chakraborti

: ‘Cu-Zn separation by supported liquid membrane analyzed through multi-objective genetic algorithms’, Hydrometallurgy , 2011, 107, 112–123.

49.

Pettersson

and Saxén

: ‘Method for the selection of inputs and structure of feedforward neural networks’, Comput. Chem. Eng. , 2006, 30, 1038–1045.

50.

Farina

and Amato

: ‘On the optimal solution definition for many-criteria optimization problems’, Proc. NAFIPS–FLINT Int. Conf. 2002, Piscataway, NJ, June 2002, IEEE Service Center, pp. 233–238.

51.

Lotov

, Kamenev

, Berezkin

and Miettinen

: ‘Optimal control of cooling process in continuous casting of steel using a visualization-based multi-criteria approach’, Appl. Math. Model. , 2005, 29, 653–672.

52.

http://www.kimeme.com/.

53.

http://www.esteco.com/modefrontier.

54.

Palani

, Murugan

and Karthikeyan

: ‘Process parameter selection for optimising weld bead geometry in stainless steel cladding using Taguchi's approach’, Mater. Sci. Technol. , 2006, 22, 1193–1200.

55.

Bigi

: ‘Uniqueness of KKT multipliers in multiobjective optimization’, Appl. Math. Lett. , 2004, 17, 1285–1290.

56.

Tutum

and Hattel

: ‘Numerical optimisation of friction stir welding: review of future challenges’, Sci. Technol. Weld. Joining , 2011, 16, 318–324.

57.

Agarwal

, Tewary

, Pettersson

, Das

, Saxén

and Chakraborti

: ‘Analysing blast furnace data using evolutionary neural network and multiobjective genetic algorithms’, Ironmaking Steelmaking , 2010, 37, 353–359.

58.

Nandan

, Rai

, Jayakanth

, Moitra

, Chakraborti

and Mukhopadhyay

: ‘Regulating crown and flatness during hot rolling: a multiobjective optimization study using genetic algorithms’, Mater. Manuf. Process. , 2005, 20, 459–478.