Advertisement

On the Impact of Representation and Algorithm Selection for Optimisation in Process Design: Motivating a Meta-Heuristic Framework

  • Eric S. FragaEmail author
  • Abdellah Salhi
  • El-Ghazali Talbi
Chapter
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)

Abstract

In an ideal world, it would be straightforward to identify the most suitable optimisation method to use in the solution of a given optimisation problem. However, although some methods may be more widely applicable than others, it is impossible a priori to know which method will work best. This may be due to the particular mathematical properties of the mathematical model, i.e. the formulation. It may also be due to the representation of the variables in the model. This combination of choices of method, representation and formulation makes it difficult to predict which combination may be best.

This paper presents an example from process engineering, the design of heat exchanger networks, for which two different representations for the same formulation are available. Two different heuristic optimisation procedures are considered. The results demonstrate that any given combination will not lead to the best outcome across a range of case studies. This motivates the need for a multi-algorithm, multi-representation approach to optimisation, at least for process design.

Keywords

Representation Optimisation Genetic algorithm Simulated annealing Heat exchanger networks 

References

  1. 1.
    C.S. Adjiman, I.P. Androulakis, C.A. Floudas, Global optimization of mixed-integer nonlinear problems. AIChE J. 46, 1769–1798 (2000)CrossRefGoogle Scholar
  2. 2.
    G. Athier, P. Floquet, L. Pibouleau, S. Domenech, Process optimization by simulated annealing and NLP procedures. Application to heat exchanger network synthesis. Comput. Chem. Eng. 21, S475–S580 (1997)Google Scholar
  3. 3.
    E.S. Fraga, Discrete optimization using string encodings for the synthesis of complete chemical processes, in State of the Art in Global Optimization: Computational Methods & Applications, ed. by C.A. Floudas, P.M. Pardalos (Kluwer, Dordrecht, 1996), pp. 627–651CrossRefGoogle Scholar
  4. 4.
    E.S. Fraga, A rewriting grammar for heat exchanger network structure evolution with stream splitting. Eng. Optim. 41, 813–831 (2009)CrossRefGoogle Scholar
  5. 5.
    E.S. Fraga, K.I.M. McKinnon, The use of dynamic programming with parallel computers for process synthesis. Comput. Chem. Eng. 18, 1–13 (1994)CrossRefGoogle Scholar
  6. 6.
    E.S. Fraga, A. Z̆ilinskas, Evaluation of hybrid optimization methods for the optimal design of heat integrated distillation sequences. Adv. Eng. Softw. 34, 73–86 (2003)Google Scholar
  7. 7.
    E.S. Fraga, J. Hagemann, A.D. Estrada Villagrana, I.D.L. Bogle, Incorporation of dynamic behaviour in an automated process synthesis system. Comput. Chem. Eng. 24, 189–194 (2000)CrossRefGoogle Scholar
  8. 8.
    E.S. Fraga, R. Patel, G.W.A. Rowe, A visual representation of process heat exchange as a basis for user interaction and stochastic optimization. Chem. Eng. Res. Des. 79, 765–776 (2001)CrossRefGoogle Scholar
  9. 9.
    K.C. Furman, N.V. Sahinidis, A critical review and annotated bibliography for heat exchanger network synthesis in the 20th century. Ind. Eng. Chem. Res. 41, 2335–2370 (2002)CrossRefGoogle Scholar
  10. 10.
    J.A.J. Hall, K.I.M. McKinnon, The simplex examples where the simplex method cycles and conditions where expand fails to prevent cycling. Math. Program. 100, 133–150 (2004). doi:10.1007/s10107-003-0488-1CrossRefGoogle Scholar
  11. 11.
    A.J. Isafiade, D.M. Fraser, Interval based MINLP superstructure synthesis of heat exchanger networks for multi-period operations. Chem. Eng. Res. Des. 88, 1329–1341 (2010)CrossRefGoogle Scholar
  12. 12.
    D.R. Lewin, A generalized method for HEN synthesis using stochastic optimization – II. The synthesis of cost-optimal networks. Comput. Chem. Eng. 22, 1387–1405 (1998)Google Scholar
  13. 13.
    B. Lin, D.C. Miller, Solving heat exchanger network synthesis problems with Tabu Search. Comput. Chem. Eng. 28, 1451–1464 (2004)CrossRefGoogle Scholar
  14. 14.
    X. Luo, Q.Y. Wen, G. Fieg, A hybrid genetic algorithm for synthesis of heat exchanger networks. Comput. Chem. Eng. 33, 1169–1181 (2009)CrossRefGoogle Scholar
  15. 15.
    W. Morton, Optimisation of a heat exchanger network superstructure using nonlinear programming. Proc. Inst. Mech. Eng. Part E 216, 89–104 (2002)CrossRefGoogle Scholar
  16. 16.
    A. Pariyani, A. Gupta, P. Ghosh, Design of heat exchanger networks using randomized algorithm. Comput. Chem. Eng. 30, 1046–1053 (2006)CrossRefGoogle Scholar
  17. 17.
    M.A.S.S. Ravagnani, A.P. Silva, P.A. Arroyo, A.A. Constantino, Heat exchanger network synthesis and optimisation using genetic algorithm. Appl. Therm. Eng. 25, 1003–1017 (2005)CrossRefGoogle Scholar
  18. 18.
    G.W.A. Rowe, E.S. Fraga, Co-operating ant swarm model for heat exchanger network design, in Adaptive Computing in Design and Manufacture VII, ed. by I.C. Parmee (The Institute for People-Centred Computation, Bristol, 2006), pp. 29–35Google Scholar
  19. 19.
    A. Salhi, The ultimate solution approach to intractable problems, in Proceedings of the 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA2010), Universiti Tunku Abdul Rahman (2010), pp. 84–93Google Scholar
  20. 20.
    A. Salhi, J.A. Vazquez-Rodriguez, Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions. J. Memetic Comput. (2014). doi:10.1007/s12293-013-012107Google Scholar
  21. 21.
    M. Serna, A. Jimenez, An area targeting algorithm for the synthesis of heat exchanger networks. Chem. Eng. Sci. 59, 2517–2520 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Eric S. Fraga
    • 1
    Email author
  • Abdellah Salhi
    • 2
  • El-Ghazali Talbi
    • 3
  1. 1.Department of Chemical EngineeringCentre for Process Systems Engineering, UCLLondonUK
  2. 2.University of EssexColchesterUK
  3. 3.INRIA Laboratory, CRISTAL/CNRSUniversity Lille 1Villeneuve d’AscqFrance

Personalised recommendations