Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems
<|reference_start|>Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve NP-Hard Problems: Many water-based optimization metaheuristics have been introduced during the last decade, both for combinatorial and for continuous optimization. Despite the strong similarities of these methods in terms of their underlying natural metaphors (most of them emulate, in some way or another, how drops collaboratively form paths down to the sea), in general the resulting algorithms are quite different in terms of their searching approach or their solution construction approach. For instance, each entity may represent a solution by itself or, alternatively, entities may construct solutions by modifying the landscape while moving. A researcher or practitioner could assume that the degree of similarity between two water-based metaheuristics heavily depends on the similarity of the natural water mechanics they emulate, but this is not the case. In order to bring some clarity to this mosaic of apparently related metaheuristics, in this paper we introduce them, explain their mechanics, and highlight their differences.<|reference_end|>
@article{rubio2024water-based,
    title={Water-Based Metaheuristics: How Water Dynamics Can Help Us to Solve
  NP-Hard Problems},
    author={Fernando Rubio and Ismael Rodr'iguez},
    journal={arXiv preprint arXiv:2403.12058},
    year={2024},
    doi={10.1155/2019/4034258},
    archivePrefix={arXiv},
    eprint={2403.12058},
    primaryClass={cs.NE cs.AI}
}