Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Ana
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Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations Daniel Molina1
· Javier Poyatos1 · Javier Del Ser2,3,4 · Salvador Garc´ıa1 · Amir Hussain5 · Francisco Herrera1,5
Received: 27 September 2019 / Accepted: 14 May 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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 natureinspired 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. Keywords Nature-inspired algorithms · Bio-inspired optimization · Taxonomy · Classification
Introduction In years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This many real-world optimization problems, no exact solver can be applied to solve them at an affordable computational cost or within a reasonable time, due to their complexity or the amount of data to use. In such cases the use of traditional techniques has been widely proven to be unsuccessful, thereby calling for the consideration of alternative optimization approaches. In this context, complexity is not unusual in Nature: a plethora of complex systems, processes and behaviors have evinced a surprising performance to efficiently address
Daniel Molina
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Extended author information available on the last page of the article.
intricate optimization tasks. The most clear example can be found in the different animal species, which have developed over generations very specialized capabilities by virtue of evolutionary mechanisms. Indeed, evolu
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