Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms
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In recent years, there has been an explosion of new metaheuristic algorithms that explore different sources of inspiration within the biological and natural worlds. A particular issue with this approach is the tendency for authors to use terminology that is derived from the domain of inspiration, rather than the broader domains of metaheuristics and optimisation. This, in turn, makes it difficult to both comprehend these algorithms and understand their relationships to other metaheuristics. This guide attempts to address this issue, at least to some extent, by providing descriptions of the 32 most cited nature-inspired algorithms published in the last 20 years using standard metaheuristic terms. It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation.
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