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Cognitive Computation

, Volume 10, Issue 4, pp 517–544 | Cite as

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

  • Daniel Molina
  • Antonio LaTorre
  • Francisco Herrera
Article

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.

Keywords

Continuous optimization Global optimization Large-scale global optimization Multimodal optimization Real-parameter competitions 

Notes

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).

Compliance with Ethical Standards

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of GranadaGranadaSpain
  2. 2.Center for Computational SimulationUniversidad Politécnica de MadridMadridSpain

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