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== Motivation ==
Knowledge of multiple solutions to an optimization task is especially helpful in engineering, when due to physical (and/or cost) constraints, the best results may not always be realizable. In such a scenario, if multiple solutions (locally and/or globally optimal) are known, the implementation can be quickly switched to another solution and still obtain the best possible system performance. Multiple solutions could also be analyzed to discover hidden properties (or relationships) of the underlying optimization problem, which makes them important for obtaining [[___domain knowledge]]. In addition, the algorithms for multimodal optimization usually not only locate multiple optima in a single run, but also preserve their population diversity, resulting in their global optimization ability on multimodal functions. Moreover, the techniques for multimodal optimization are usually borrowed as diversity maintenance techniques to other problems.<ref>Wong, K. C. et al. (2012), [https://dx.doi.org/10.1016/j.ins.2011.12.016 Evolutionary multimodal optimization using the principle of locality] Information Sciences</ref><ref>{{Cite journal |last=Jiang |first=Yi |last2=Zhan |first2=Zhi-Hui |last3=Tan |first3=Kay Chen |last4=Zhang |first4=Jun |date=April 2023 |title=Optimizing Niche Center for Multimodal Optimization Problems |url=https://ieeexplore.ieee.org/document/9655447/ |journal=IEEE Transactions on Cybernetics |volume=53 |issue=4 |pages=2544–2557 |doi=10.1109/TCYB.2021.3125362 |issn=2168-2267|doi-access=free }}</ref>
== Background ==
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