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Evolutionary multimodal optimization
In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple solutions (as opposed to a single best solution) to a problem.〔Wong, K. C. (2015), (Evolutionary Multimodal Optimization: A Short Survey ) arXiv preprint arXiv:1508.00457〕
== 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 (local and global) are known, the implementation can be quickly switched to another solution and still obtain an optimal system performance. Multiple solutions could also be analyzed to discover hidden properties (or relationships), which makes them high-performing.
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.〔Wong, K. C. et al. (2012), (Evolutionary multimodal optimization using the principle of locality ) Information Sciences〕
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