Skip to main content
SpringerLink
Log in

Hybridization Approach Towards Improving the Performance of Evolutionary Algorithm

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Multi-objective differential evolution (MODE) algorithm has been widely used in solving multi-objective optimization problems. In this paper, a hybridization technique is proposed to improve the performance of MODE algorithm in terms of speed and convergence. The proposed hybrid MODE-dynamic-random local search (HMODE-DLS) algorithm combines MODE and dynamic-random local search (DLS) algorithm. To evaluate the proposed algorithm and validate its performance, benchmark test problems (both constrained and non-constrained) are considered to be solved using MODE and the proposed HMODE-DLS algorithms. To compare between the two algorithms, five performance metrices are calculated, which are convergence, spread, generational distance, spacing and hypervolume ratio. Mean and standard deviation values for the performance metrics are reported, and the best in each category is highlighted. The Conv metric results of the new hybrid MODE are compared with other reported ones. Additionally, the effect of local search probability is studied for selected problems. In general, HMODE-DLS performance outshines, in terms of convergence and robustness, compared with other tested algorithms. HMODE-DLS is, generally, faster, and its results are of improved quality compared to MODE algorithm.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Chiandussi, G.; Codegone, M.; Ferrero, S.; Varesio, F.E.: Comparison of multi-objective optimization methodologies for engineering applications. Comput. Math. Appl. 63(5), 912–942 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  2. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, London (2002)

    MATH  Google Scholar 

  3. Gujarathi, A.M.; Babu, B.: Evolutionary Computation: Techniques and Applications. CRC Press, New York (2016)

    Book  MATH  Google Scholar 

  4. Coello, C.A.C.; Lamont, G.B.; Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-objective Problems, vol. 5. Springer, Berlin (2007)

    MATH  Google Scholar 

  5. Deb, K.; Agrawal, S.; Pratap, A.; Meyarivan, T.: 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. Springer, Berlin (2000)

  6. Zitzler, E.; Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evolut. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  7. Coello, C.A.C.; Lamont, G.B.: Applications of Multi-objective Evolutionary Algorithms, vol. 1. World Scientific, Singapore (2004)

    Book  MATH  Google Scholar 

  8. Khatibi, S.; Bafruei, M.K.; Rahmani, M.: Modelling a bi-objective airport gate scheduling with controllable processing time using hybrid NSGA-II and VNS algorithm. Int. J. Oper. Res. 34(1), 1–27 (2019)

    Article  MathSciNet  Google Scholar 

  9. Silva, A.S.D.; Ma, H.; Mei, Y.; Zhang, M.: A hybrid memetic approach for fully automated multi-objective web service composition. In: 2018 IEEE International Conference on Web Services (ICWS), 2–7 July 2018, pp. 26–33 (2018)

  10. Wang, C.; Ma, H.; Chen, G.; Hartmann, S.: A memetic NSGA-II with EDA-based local search for fully automated multiobjective web service composition. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 421–422 (2019)

  11. Zhang, M.; Wang, H.; Cui, Z.; Chen, J.: Hybrid multi-objective cuckoo search with dynamical local search. Memet. Comput. 10(2), 199–208 (2018)

    Article  Google Scholar 

  12. Dulebenets, M.A.: An adaptive island evolutionary algorithm for the berth scheduling problem. Memet. Comput. 12(1), 51–72 (2020)

    Article  Google Scholar 

  13. Fu, Y.; Wang, H.; Tian, G.; Li, Z.; Hu, H.: Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm. J. Intell. Manuf. 30(5), 2257–2272 (2019)

    Article  Google Scholar 

  14. Zhang, W.; Wang, Y.; Yang, Y.; Gen, M.: Hybrid multiobjective evolutionary algorithm based on differential evolution for flow shop scheduling problems. Comput. Ind. Eng. 130, 661–670 (2019)

    Article  Google Scholar 

  15. Gujarathi, A.M.; Babu, B.V.: Multi-objective optimization of industrial styrene reactor: adiabatic and pseudo-isothermal operation. Chem. Eng. Sci. 65(6), 2009–2026 (2010)

    Article  Google Scholar 

  16. Gujarathi, A.M.; Babu, B.: Optimization of adiabatic styrene reactor: a hybrid multiobjective differential evolution (H-MODE) approach. Ind. Eng. Chem. Res. 48(24), 11115–11132 (2009)

    Article  Google Scholar 

  17. Babu, B.; Gujarathi, A.M.; Katla, P.; Laxmi, V.: Strategies of multi-objective differential evolution (MODE) for optimization of adiabatic styrene reactor. In: Proceedings of the International Conference on Emerging Mechanical Technology: Macro to Nano (EMTMN-2007), p. 243 (2007)

  18. Gujarathi, A.M.; Babu, B.V.: Multiobjective optimization of Industrial processes using elitist multiobjective differential evolution (Elitist-MODE). Mater. Manuf. Processes 26(3), 455–463 (2011)

    Article  Google Scholar 

  19. Gujarathi, A.M.; Babu, B.: Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization. In: IICAI, pp. 933–948 (2009)

  20. Al-Siyabi, B.; Gujarathi, A.M.; Sivakumar, N.: Harmonic multi-objective differential evolution approach for multi-objective optimization of fed-batch bioreactor. Mater. Manuf. Processes 32(10), 1152–1161 (2017)

    Article  Google Scholar 

  21. Sharma, S.; Rangaiah, G.P.: An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput. Chem. Eng. 56, 155–173 (2013)

    Article  Google Scholar 

  22. Xiao, J.; Wang, K.: Ranking-based elitist differential evolution for many-objective optimization. In: 2013 5th International Conference on Intelligent Human–Machine Systems and Cybernetics, pp. 310–313. IEEE (2013)

  23. Dong, N.; Wang, Y.: Hybrid multiobjective differential evolution incorporating preference based local search. Int. J. Comput. Int. Syst. 7(4), 733–747 (2014)

    Article  Google Scholar 

  24. Dong, N.; Wang, Y.: A hybrid multiobjective differential evolution algorithm based on improved e-dominance. In: 2011 Seventh International Conference on Computational Intelligence and Security, pp. 24–28. IEEE (2011)

  25. Lin, Q.; Zhu, Q.; Huang, P.; Chen, J.; Ming, Z.; Yu, J.: A novel hybrid multi-objective immune algorithm with adaptive differential evolution. Comput. Oper. Res. 62, 95–111 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  26. Tang, L.; Wang, X.: A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems. IEEE Trans. Evolut. Comput. 17(1), 20–45 (2013)

    Article  MathSciNet  Google Scholar 

  27. Percus, A.; Istrate, G.; Moore, C.: Computational Complexity and Statistical Physics. Oxford University Press, Oxford (2006)

    MATH  Google Scholar 

  28. Onwubolu, G.C.; Babu, B.: New Optimization Techniques in Engineering, vol. 141. Springer, Heidelberg (2013)

    MATH  Google Scholar 

  29. Storn, R.; Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  30. Babu, B.; Chakole, P.G.; Mubeen, J.S.: Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor. Chem. Eng. Sci. 60(17), 4822–4837 (2005)

    Article  Google Scholar 

  31. Babu, B.; Mubeen, J.S.; Chakole, P.G.: Simulation and optimization of wiped-film poly-ethylene terephthalate (PET) reactor using multiobjective differential evolution (MODE). Mater. Manuf. Processes 22(5), 541–552 (2007)

    Article  Google Scholar 

  32. Reddy, M.J.; Kumar, D.N.: Multiobjective differential evolution with application to reservoir system optimization. J. Comput. Civ. Eng. 21(2), 136–146 (2007)

    Article  Google Scholar 

  33. Gujarathi, A.M.; Babu, B.: Improved multiobjective differential evolution (MODE) approach for purified terephthalic acid (PTA) oxidation process. Mater. Manuf. Process. 24(3), 303–319 (2009)

    Article  Google Scholar 

  34. Huband, S.; Hingston, P.; Barone, L.; While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evolut. Comput. 10(5), 477–506 (2006)

    Article  MATH  Google Scholar 

  35. Zitzler, E.; Deb, K.; Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  36. Rangaiah, G.P.: Multi-objective Optimization: Techniques and Applications in Chemical Engineering, vol. 1. World Scientific, Singapore (2009)

    Google Scholar 

  37. Michalewicz, Z.: A survey of constraint handling techniques in evolutionary computation methods. In: Proceedings of the 4th Annual Conference on Evolutionary Programming, pp. 135–155 (1995)

  38. Yeniay, Ö.: Penalty function methods for constrained optimization with genetic algorithms. Math. Comput. Appl. 10(1), 45–56 (2005)

    MathSciNet  Google Scholar 

  39. Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, vol. 63. Citeseer, Zurich (1999)

    Google Scholar 

  40. Veldhuizen, D.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. Write-Patterson: Air Force Institute of Technology (1999)

  41. Schott, J.R.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Massachusetts Institute of Technology, Cambridge (1995)

    Google Scholar 

  42. Wang, Y.-N.; Wu, L.-H.; Yuan, X.-F.: Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure. Soft Comput. 14(3), 193 (2010)

    Article  Google Scholar 

  43. Gujarathi, A.M.; Babu, B.: Hybrid strategy of multi-objective differential evolution (H-MODE) for multi-objective optimisation. Int. J. Comput. Int. Stud. 2(2), 157–186 (2013)

    Google Scholar 

  44. Fonseca, C.M.; Guerreiro, A.P.; López-Ibáñez, M.; Paquete, L.: On the computation of the empirical attainment function. In: International Conference on Evolutionary Multi-criterion Optimization, pp. 106–120. Springer, Berlin (2011)

  45. Conover, W.: Practical Nonparametric Statistics. Wiley, New York (1999)

    Google Scholar 

  46. de Souza, T.C.; Goldbarg, E.F.; Goldbarg, M.C.: Comparing PSO and NSGA II for the biobjective oil derivatives distribution problem. In: IEEE Congress on Evolutionary Computation, pp. 1–7. IEEE (2010)

  47. Deb, K.: Multi Objective Optimization Using Evolutionary Algorithms. Wiley, London (2001)

    MATH  Google Scholar 

Download references

Funding

This work was partially supported by Sultan Qaboos University, Sultanate of Oman under Grant IG/ENG/PCED/19/01.

Author information

Authors and Affiliations

  1. Department of Petroleum and Chemical Engineering, College of Engineering, Sultan Qaboos University, Al-Khod, P.O. Box 33, 123, Muscat, Sultanate of Oman

    Zainab Al Ani, Ashish M. Gujarathi, G. Reza Vakili-Nezhaad & Ala’a H. Al-Muhtaseb

Authors
  1. Zainab Al Ani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashish M. Gujarathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Reza Vakili-Nezhaad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ala’a H. Al-Muhtaseb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish M. Gujarathi.

Appendix

Appendix

See Tables 6, 7 and 8.

Table 6 Four performance metrics results for HMODE-DLS with different Fl values and MODE algorithm for non-constrained test problems after 250 generations
Full size table
Table 7 Reported mean Conv performance metric results for different algorithms for non-constrained test problems [47]
Full size table
Table 8 Comparison between reported mean Conv metric results for different algorithms (Table 7) and HMODE-DLS (Table 6) for non-constrained test problems
Full size table
Fig. 11
figure 11

Pareto front for MODE and HMODE-DLS algorithms for unconstrained test problems

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al Ani, Z., Gujarathi, A.M., Vakili-Nezhaad, G.R. et al. Hybridization Approach Towards Improving the Performance of Evolutionary Algorithm. Arab J Sci Eng 45, 11065–11086 (2020). https://doi.org/10.1007/s13369-020-04964-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-04964-y

Keywords

Search

Navigation