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An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions

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Abstract

Over the recent years, continuous optimization has significantly evolved to become the mature research field it is nowadays. Through this process, evolutionary algorithms had an important role, as they are able to obtain good results with limited resources. Among them, bio-inspired algorithms, which mimic cooperative and competitive behaviors observed in animals, are a very active field, with more proposals every year. This increment in the number of optimization algorithms is apparent in the many competitions held at corresponding special sessions in the last 10 years. In these competitions, several algorithms or ideas have become points of reference, and used as starting points for more advanced algorithms in following competitions. In this paper, we have obtained, for different real-parameter competitions, their benchmarks, participants, and winners (from the competitions’ website) and we review the most relevant algorithms and techniques, presenting the trajectory they have followed over the last years and how some of these works have deeply influenced the top performing algorithms of today. The aim is to be both a useful reference for researchers new to this interesting research topic and a useful guide for current researchers in the field. We have observed that there are several algorithms, like the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE), Mean-Variance Mapping Optimization (MVMO), and Multiple Offspring Sampling (MOS), which have obtained a strong influence over other algorithms. We have also suggested several techniques that are being widely adopted among the winning proposals, and that could be used for more competitive algorithms. Global optimization is a mature research field in continuous improvement, and the history of competitions provides useful information about the past that can help us to learn how to go forward in the future.

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Notes

  1. http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm for the CEC’2005 competitions and http://web.mysites.ntu.edu.sg/epnsugan/PublicSite/Shared%20Documents/Forms/AllItems.aspx for the rest of CEC special sessions

  2. http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC-05/CEC05.htm

  3. http://www3.ntu.edu.sg/home/epnsugan/index_files/CEC11-RWP/CEC11-RWP.htm

  4. http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm

  5. http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2014/CEC2014.htm

  6. http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2015/CEC2015.htm

  7. http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2016/CEC2016.htm

  8. http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2017/CEC2017.htm

  9. http://coco.gforge.inria.fr/doku.php?id=algorithms

  10. http://coco.gforge.inria.fr/doku.php?id=bbob-2009-algorithms

  11. http://coco.gforge.inria.fr/doku.php?id=bbob-2010

  12. http://coco.gforge.inria.fr/doku.php?id=bbob-2012

  13. http://coco.gforge.inria.fr/doku.php?id=bbob-2013

  14. http://coco.gforge.inria.fr/doku.php?id=bbob-2015

  15. https://numbbo.github.io/workshops/BBOB-2016/

  16. https://numbbo.github.io/workshops/BBOB-2017/

  17. http://bbcomp.ini.rub.de/index.html

  18. see http://bbcomp.ini.rub.de/results/BBComp2015GECCO/summary.html

  19. https://www.artelys.com/en/optimization-tools/knitro

  20. available at https://www.uni-due.de/mvmo/download

  21. at https://github.com/ash-aldujaili/NMSO

  22. see http://bbcomp.ini.rub.de/results/BBComp2015CEC/summary.html

  23. at http://bbcomp.ini.rub.de/results/BBComp2015CEC/mickey.zip

  24. at http://ls11-www.cs.uni-dortmund.de/staff/wessing/bbcomp

  25. at http://ls11-www.cs.uni-dortmund.de/staff/wessing/bbcomp

  26. http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-06/CEC06.htm

  27. http://www3.ntu.edu.sg/home/EPNSugan/index_files/CEC10-Const/CEC10-Const.htm

  28. https://github.com/mikeagn/CEC2013/

  29. https://github.com/fieldsend

  30. http://goanna.cs.rmit.edu.au/~xiaodong/cec16-niching/

  31. http://www.husseinabbass.net/BigOpt.html

  32. at https://sites.google.com/site/tanaberyoji/home

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Acknowledgments

This work was supported by grants from the Spanish Ministry of Science and the European Fund (FEDER) under projects (TIN2014-57481-C2-2-R, TIN2016-8113-R, TIN2017-83132-C2-2-R, TIN2017-89517-P) and Regional Government (P12-TIC-2958).

Funding

This work was supported by grants from the Spanish Ministry of Science and the European Fund (FEDER) under projects (TIN2014-57481-C2-2-R, TIN2016-8113-R, TIN2017-83132-C2-2-R, TIN2017-89517-P) and Regional Government (P12-TIC-2958).

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Authors and Affiliations

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

    Daniel Molina & Francisco Herrera

  2. Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain

    Antonio LaTorre

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  1. Daniel Molina
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Correspondence to Daniel Molina.

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Appendix: List of algorithms in competitions

Appendix: List of algorithms in competitions

In order to summarize the most relevant algorithms in the different competitions covered by the paper, Tables 2, 3, 4, 5, and 6 show this information, grouped by benchmark and conference and ordered according to their ranking in the corresponding competitions. In particular, Tables 2 and 3 show the more relevant algorithms in global optimization for the different CEC and BBOB benchmarks, respectively. On the other hand, Table 4 shows the algorithms in the Black-Box Competition for the different conferences, whereas Table 5 shows the algorithms for other competitions such as constraint and multimodal optimization. Finally, Table 6 summarizes the information of the algorithms for large-scale global optimization.

Table 2 List of algorithms in CEC global competitions (sorted by ranking)
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Table 3 List of the most relevant algorithms in the Black-Box Optimization Benchmark competitions
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Table 4 List of the most relevant algorithms in the Black-Box Benchmark competitions (BBComp)
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Table 5 List of the most relevant algorithms in other competitions
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Table 6 List of the most relevant algorithms in Large-Scale Global Optimization competitions
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Molina, D., LaTorre, A. & Herrera, F. An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions. Cogn Comput 10, 517–544 (2018). https://doi.org/10.1007/s12559-018-9554-0

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