Classifying Metaheuristics: Towards a unified multi-level classification system

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 capabilit...

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Bibliographic Details
Published in:Natural computing 2022-06, Vol.21 (2), p.155-171
Main Authors: Stegherr, Helena, Heider, Michael, Hähner, Jörg
Format: Article
Language:English
Subjects:
Artificial Intelligence
Classification
Complex Systems
Computer Science
Criteria
Evolutionary Biology
Heuristic methods
Optimization
Processor Architectures
Theory of Computation
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Summary: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.
ISSN:1567-7818
1572-9796
DOI:10.1007/s11047-020-09824-0