A PSO-inspired architecture to hybridise multi-objective metaheuristics

Abstract

Hybridisation is a technique that exploits and unites the best features of individual algorithms. The literature includes several hybridisation methodologies, among which there are general procedures, termed architectures, that provide generic functionalities and features for solving optimisation problems. Successful hybridisation methodologies have applied concepts of the multi-agent paradigm, such as cooperation and agent intelligence. However, there is still a lack concerning architectures for the hybridisation of multi-objective metaheuristics that fully explore these concepts. This study proposes a new architecture, named MO-MAHM, based on concepts from Particle Swarm Optimisation, to hybridise multi-objective metaheuristics. We apply the MO-MAHM to the Bi-objective Spanning Tree Problem. Four algorithms were hybridised within the MO-MAHM: three evolutionary algorithms and a local search method. We report the results of experiments with 180 instances, analyse the behaviour of the MO-MAHM, and compare to the results produced by algorithms proposed for the Bi-objective Spanning Tree Problem.

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References

  1. 1.

    Aggarwal V, Aneja Y, Nair K (1982) Minimal spanning tree subject to a side constraint. Comput Oper Res 9(4):287–296

    Article  Google Scholar 

  2. 2.

    Arroyo JEC, Vieira PS, Vianna DS (2008) A GRASP algorithm for the multi-criteria minimum spanning tree problem. Ann Oper Res 159(1):125–133

    MathSciNet  Article  Google Scholar 

  3. 3.

    Aydin ME (2012) Coordinating metaheuristic agents with swarm intelligence. J Intell Manuf 23(4):991–999

    Article  Google Scholar 

  4. 4.

    Bleuler A, Laumanns M, Thiele L, Zitzler E (2003) PISA—a platform and programming language independent interface for search algorithms. In: Fonseca CM, Fleming PJ, Zitzler E, Deb K, Thiele L (eds) Evolutionary multi-criterion optimization (EMO 2003). Springer, Berlin, pp 494–508

    Google Scholar 

  5. 5.

    Chen G, Chen S, Guo W, Chen H (2007) The multi-criteria minimum spanning tree problem based genetic algorithm. Inf Sci 117(22):5050–5063

    MathSciNet  Article  Google Scholar 

  6. 6.

    Conover WJ (1980) Practical nonparametric statistics. Wiley, New York

    Google Scholar 

  7. 7.

    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  8. 8.

    Dubois-Lacoste J, López-Ibáñez M, Stützle T (2015) Anytime pareto local search. Eur J Oper Res 243(2):369–385

    MathSciNet  Article  Google Scholar 

  9. 9.

    Fernandes IFC, Goldbarg EFG, Maia SMDM, Goldbarg MC (2020) Empirical study of exact algorithms for the multi-objective spanning tree. Comput Optim Appl 75(2):561–605

    MathSciNet  Article  Google Scholar 

  10. 10.

    Glover F, Laguna M, Marti R (2000) Fundamentals of scatter search and path relinking. Control Cybern 29(3):653–684

    MathSciNet  MATH  Google Scholar 

  11. 11.

    Goldbarg EFG, Goldbarg MC (2009) Transgenetic algorithm: a new endosymbiotic approach for evolutionary algorithms. In: Abraham A et al (eds) Foundations of computational intelligence, vol 3. Springer, Berlin, pp 425–460

    Google Scholar 

  12. 12.

    Hammami M, Bechikh S, Hung C, Said LB (2019) A multi-objective hybrid filter-wrapper evolutionary approach for feature selection. Memet Comput 11(2):193–208

    Article  Google Scholar 

  13. 13.

    Knowles JD (2002) Local-search and hybrid evolutionary algorithms for Pareto optimization. Ph.D. Thesis, Unpublished Ph.D. Thesis, Department of Computer Science, University of Reading, Reading, UK

  14. 14.

    Knowles JD, Thiele L, Zitzler E (2005) A tutorial on the performance assessment of stochastic multiobjective optimizers, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Swiss, vol 214

  15. 15.

    López-Ibáñez M, Dubois-Lacoste J, Cáceres LP, Birattari M, Stützle T (2016) The irace package: iterated racing for automatic algorithm configuration. Oper Res Perspect 3:43–58

    MathSciNet  Article  Google Scholar 

  16. 16.

    Monteiro SMD, Goldbarg EFG, Goldbarg MC (2010) A new transgenetic approach for the biobjective spanning tree problem. In: IEEE CEC 2010: proceedings of IEEE congress on evolutionary computation Barcelona, Spain, pp 519–526

  17. 17.

    Prim RC (1957) Shortest connection networks and some generalizations. Bell Syst Techn J 36(6):1389–1401

    Article  Google Scholar 

  18. 18.

    Raidl GR, Julstrom BA (2003) Edge sets: an effective evolutionary coding of spanning trees. IEEE Trans Evol Comput 7(3):225–239

    Article  Google Scholar 

  19. 19.

    Ruzika S, Hamacher HW (2009) A survey on multiple objective minimum spanning tree problems. In: Lerner J et al (eds) Algorithmics of large and complex networks. Springer, Heidelberg, pp 104–116

    Google Scholar 

  20. 20.

    Rocha DAM, Goldbarg EFG, Goldbarg MC (2006) A memetic algorithm for the biobjective minimum spanning tree problem. In: EvoCOP 2006: proceedings of 6th European conference on evolutionary computation in combinatorial optimization. Budapest, Hungary, pp 222–233

  21. 21.

    Sattar A, Seguier R (2010) HMOAM: hybrid multi-objective genetic optimization for facial analysis by appearance model. Memet Comput 2(1):25–46

    Article  Google Scholar 

  22. 22.

    Silva IRM, Goldbarg EFG, Carvalho EB, Goldbarg MC (2017) A parallel Multi-agent Architecture for Hybridization of metaheuristics for multi-objective problems. In: IEEE CEC 2017: proceedings of IEEE congress on evolutionary computation. San Sebastián, Spain, pp 580–587

  23. 23.

    Silva MAL, Souza SR, Souza MJF, França Filho MF (2018) Hybrid metaheuristics and multi-agent systems for solving optimization problems: a review of frameworks and a comparative analysis. Appl Soft Comput 71:433–459

    Article  Google Scholar 

  24. 24.

    Souza GR, Goldbarg EFG, Canuto AMP, Goldbarg MC, Ramos ICO (2018) MAHM: a PSO-based multiagent architecture for hybridisation of metaheuristics. In: Tan Y (ed) Swarm intelligence—volume 1: principles, current algorithms and methods, pp 237–264

  25. 25.

    Taillard ÉD, Waelti P, Zuber J (2008) Few statistical tests for proportions comparison. Eur J Oper Res 185(3):1336–1350

    Article  Google Scholar 

  26. 26.

    Talbi E (2015) Hybrid metaheuristics for multi-objective optimization. J Algorithms Comput Technol 9(1):41–63

    MathSciNet  Article  Google Scholar 

  27. 27.

    Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Parallel problem solving from nature, PPSN V: 5th international conference. The Netherlands, Amsterdam, pp 292–301

  28. 28.

    Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm, TIK-report, Eidgenössische Technische Hochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze (TIK), vol 103

  29. 29.

    Zitzler E, Thiele L, Laumanns M, Fonseca CM, Da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2):117–132

    Article  Google Scholar 

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Acknowledgements

We thank Arroyo, Vieira and Viana who kindly provided us the instances used to test the GRASP algorithm. This research was partially supported by the High Performance Computing Center at Universidade Federal do Rio Grande do Norte (NPAD/UFRN), by the Coordination for the Improvement of Higher Education Personnel (CAPES), and by the National Council for Scientific and Technological Development (CNPq), Brazil, under Grants 302387/2016-1 and 306702/2017-7.

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Correspondence to I. F. C. Fernandes.

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Fernandes, I.F.C., Silva, I.R.M., Goldbarg, E.F.G. et al. A PSO-inspired architecture to hybridise multi-objective metaheuristics. Memetic Comp. (2020). https://doi.org/10.1007/s12293-020-00307-4

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Keywords

  • Multi-objective optimisation
  • Hybridisation of metaheuristics
  • Bi-objective spanning tree