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

Molina, Daniel; Poyatos, Javier; Ser, Javier Del; García, Salvador; Hussain, Amir; Herrera, Francisco

doi:10.1007/s12559-020-09730-8

Cited by 160 publications

(103 citation statements)

References 330 publications

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“…This approach can be used for framework classification schemes in general, as it is an efficient way to find similarities in heuristic usage and highlight different strategies when selecting the use of these heuristics. Molina et al (2020) provide extensive and insightful new approaches on taxonomies of metaheuristics. The first proposed taxonomy classifies metaheuristics according to their metaphor and represents a categorisation.…”

Section: Classification Schemes In Literaturementioning

confidence: 99%

“…This structure provides a basic overview on different kinds of metaphor-inspiration. In addition to this categorisation, Molina et al (2020) provide a classification system based on the behaviour of the metaheuristics. This may be the first attempt to use criteria relating to the overall behaviour of metaheuristic frameworks.…”

Section: Classification Schemes In Literaturementioning

confidence: 99%

“…They present a taxonomy distinguishing the number of high-fitness individuals in the metaheuristic, single-elite, multielite or none-elite. This approach is similar to the classification of Molina et al (2020), especially to their description of differential vector movement metaheuristics.…”

Section: Classification Schemes In Literaturementioning

confidence: 99%

“…Preferably, a general and unified framework is devised, in which all metaheuristics can be included (Nesmachnow 2014;Del Ser et al 2019;Molina et al 2020). This would facilitate direct comparisons and evaluations, as well as the development and improvement of metaheuristics.…”

Section: How Metaheuristics Should Be Treatedmentioning

confidence: 99%

“…Other important research areas (e.g. further improvements, theory) in the field of metaheuristics are tended to only sparsely (Sörensen 2013;Lones 2014;Del Ser et al 2019;Molina et al 2020). This results in a deficit in progress, especially when fundamental research on metaheuristics is concerned.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Metaheuristics: Towards a unified multi-level classification system

Stegherr¹,

Heider²,

Hähner³

2020

Nat Comput

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Metaheuristics provide the means to approximately solve complex optimisation problems when exact optimisers cannot be utilised. This led to an explosion in the number of novel metaheuristics, most of them metaphor-based, using nature as a source of inspiration. Thus, keeping track of their capabilities and innovative components is an increasingly difficult task. This can be resolved by an exhaustive classification system. Trying to classify metaheuristics is common in research, but no consensus on a classification system and the necessary criteria has been established so far. Furthermore, a proposed classification system can not be deemed complete if inherently different metaheuristics are assigned to the same class by the system. In this paper we provide the basis for a new comprehensive classification system for metaheuristics. We first summarise and discuss previous classification attempts and the utilised criteria. Then we present a multi-level architecture and suitable criteria for the task of classifying metaheuristics. A classification system of this kind can solve three main problems when applied to metaheuristics: organise the huge set of existing metaheuristics, clarify the innovation in novel metaheuristics and identify metaheuristics suitable to solve specific optimisation tasks.

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Section: Classification Schemes In Literaturementioning

confidence: 99%

Section: Classification Schemes In Literaturementioning

confidence: 99%

Section: Classification Schemes In Literaturementioning

confidence: 99%

Section: How Metaheuristics Should Be Treatedmentioning

confidence: 99%