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Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

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Abstract

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.

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Notes

  1. http://ecf.zemris.fer.hr/

  2. http://moeaframework.org/

  3. http://esa.github.io/pygmo/index.html

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Funding

This work was supported by grants from the Spanish Ministry of Science and the European Fund (FEDER) under projects TIN2016-81113R, and TIN2017-89517-P. Javier Del Ser received support from the Basque Government through the ELKARTEK and EMAITEK funding programs.

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Authors and Affiliations

  1. DaSCI Andalusian Institute of Data Science and Computation Intelligence, University of Granada, Granada, Spain

    Daniel Molina, Javier Poyatos, Salvador García & Francisco Herrera

  2. TECNALIA, Basque Research and Technology Alliance, Derio, Spain

    Javier Del Ser

  3. Dept. of Communications Engineering, University of the Basque Country (UPV/EHU), Leioa, Spain

    Javier Del Ser

  4. Edinburgh Napier University, Edinburgh, UK

    Javier Del Ser

  5. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Amir Hussain & Francisco Herrera

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  1. Daniel Molina
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Correspondence to Daniel Molina.

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AH is Editor-in-Chief, and FH is editorial board member of Cognitive Computation. They had no involvement in any aspect of the decision-making on this paper.

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Molina, D., Poyatos, J., Ser, J.D. et al. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cogn Comput 12, 897–939 (2020). https://doi.org/10.1007/s12559-020-09730-8

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