Abstract:
A notable weakness of the literature concerning foraging inspired algorithms is that little attempt is typically made to rigorously identify the similarities and differen...Show MoreMetadata
Abstract:
A notable weakness of the literature concerning foraging inspired algorithms is that little attempt is typically made to rigorously identify the similarities and differences between newly proposed algorithms and existing ones. This has led to a critique from a growing number of researchers that greater efforts need to be made to consolidate the literature on foraging algorithms (and that of metaheuristics more generally) by applying a more critical perspective when assessing the worth of both current and new metaheuristics. An important part of this process is the development of taxonomies which allow us to tease out the similarities and differences between new and existing algorithms. This paper focusses on this issue and introduces a number of taxonomies which can be used for this purpose. It also illustrates that most foraging algorithms can be encapsulated in a high level metaframework, with differing operationalisations of elements of this framework giving rise to alternative algorithms with distinct search characteristics.
Published in: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Date of Conference: 29-31 July 2017
Date Added to IEEE Xplore: 25 June 2018
ISBN Information: