Skip to main content
SpringerLink
Log in

A Comparison of Meta-heuristic Based Optimization Methods Using Standard Benchmarks

  • Conference paper
  • First Online:
Hybrid Artificial Intelligent Systems (HAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13469))

Included in the following conference series:

  • 654 Accesses

Abstract

Optimization problems are a type of problem in which multiple solutions satisfy the problem’s constraints, so not only must a good solution be found, but the objective is to find the best solution among all those considered valid. Optimization problems can be solved by using deterministic and stochastic algorithms. Those categories can be divided into different kinds of problems. One of the categories inside stochastic algorithms is metaheuristics. This work implements three well-known meta-heuristics –Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimizer–, and compares them using ten mathematical optimization problems that combine non-constrained from other studies and constrained problems from CEC2017 competition. Results show the Grey Wolf Optimizer as the method with faster convergence and best fitness for almost all the problems. This work aims to implement and compare various metaheuristics to carry out future work on solving various real-world problems.

This research has been founded by European Union’s Horizon 2020 research and innovation programme (project DIH4CPS) under the Grant Agreement no 872548. Furthermore, this research has been funded by the SUDOE Interreg Program -grant INUNDATIO-, by the Spanish Ministry of Economics and Industry, grant PID2020-112726RB-I00, by the Spanish Research Agency (AEI, Spain) under grant agreement RED2018-102312-T (IA-Biomed), by CDTI (Centro para el Desarrollo Tecnológico Industrial) under projects CER-20211003 and CER-20211022, by and Missions Science and Innovation project MIG-20211008 (INMERBOT). Also, by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994 and by ICE (Junta de Castilla y León) under project CCTT3/20/BU/0002.

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. Azimi, Z.N.: Comparison of metaheuristic algorithms for examination timetabling problem. J. Appl. Math. Comput. 16(1), 337 (2004). https://doi.org/10.1007/BF02936173

    Article  MathSciNet  MATH  Google Scholar 

  2. Bloomfield, M.W., Herencia, J.E., Weaver, P.M.: Analysis and benchmarking of meta-heuristic techniques for lay-up optimization. Comput. Struct. 88(5), 272–282 (2010). https://doi.org/10.1016/j.compstruc.2009.10.007

    Article  Google Scholar 

  3. Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 2, pp. 1470–1477 (1999). https://doi.org/10.1109/CEC.1999.782657

  4. Fadakar, E., Ebrahimi, M.: A new metaheuristic football game inspired algorithm. In: 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 6–11 (2016). https://doi.org/10.1109/CSIEC.2016.7482120

  5. Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001). https://doi.org/10.1177/003754970107600201

    Article  Google Scholar 

  6. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968). https://doi.org/10.1109/TSSC.1968.300136

    Article  Google Scholar 

  7. Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013). https://doi.org/10.1016/j.ins.2012.08.023. Including Special Section on New Trends in Ambient Intelligence and Bio-inspired Systems

    Article  MathSciNet  Google Scholar 

  8. Irizarry, R.: A generalized framework for solving dynamic optimization problems using the artificial chemical process paradigm: applications to particulate processes and discrete dynamic systems. Chem. Eng. Sci. 60(21), 5663–5681 (2005). https://doi.org/10.1016/j.ces.2005.05.028

    Article  Google Scholar 

  9. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983). https://doi.org/10.1126/science.220.4598.671

    Article  MathSciNet  MATH  Google Scholar 

  10. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015). https://doi.org/10.1016/j.advengsoft.2015.01.010

    Article  Google Scholar 

  11. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015). https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  12. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016). https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  13. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016). https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  14. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  15. Mousavirad, S.J., Ebrahimpour-Komleh, H.: Human mental search: a new population-based metaheuristic optimization algorithm. Appl. Intell. 47(3), 850–887 (2017). https://doi.org/10.1007/s10489-017-0903-6

    Article  Google Scholar 

  16. Parejo, J.A., Ruiz-Cortés, A., Lozano, S., Fernandez, P.: Metaheuristic optimization frameworks: a survey and benchmarking. Soft. Comput. 16(3), 527–561 (2012). https://doi.org/10.1007/s00500-011-0754-8

    Article  Google Scholar 

  17. Sonmez, M.: Performance comparison of metaheuristic algorithms for the optimal design of space trusses. Arab. J. Sci. Eng. 43(10), 5265–5281 (2018). https://doi.org/10.1007/s13369-018-3080-y

    Article  Google Scholar 

  18. Williams, M.L., Wilson, R.C., Hancock, E.R.: Deterministic search for relational graph matching. Pattern Recogn. 32(7), 1255–1271 (1999). https://doi.org/10.1016/S0031-3203(98)00152-6

    Article  Google Scholar 

  19. Wu, G., Mallipeddi, R., Suganthan, P.: Problem definitions and evaluation criteria for the CEC 2017 competition and special session on constrained single objective real-parameter optimization. Technical report, IEEE Congress on Evolutionary Computation (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Oviedo, Oviedo, Spain

    Enol García & José R. Villar

  2. Department of Computer Science, University of Babes Boliay, Cluj-Napocav, Romania

    Camelia Chira

  3. Instituto Tecnológico de Castilla y León, Burgos, Spain

    Javier Sedano

Authors
  1. Enol García
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José R. Villar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Camelia Chira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javier Sedano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enol García .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  4. University of Oviedo, Oviedo, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Analytics Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Applied Computational Intelligence, University of Burgos, Burgos, Burgos, Spain

    Álvaro Herrero

  8. Universidad Pablo de Olavide, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

García, E., Villar, J.R., Chira, C., Sedano, J. (2022). A Comparison of Meta-heuristic Based Optimization Methods Using Standard Benchmarks. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15471-3_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15470-6

  • Online ISBN: 978-3-031-15471-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation