Evolutionary multimodal optimization: Difference between revisions

Revision as of 11:01, 8 December 2015

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In [[applied mathematics]], '''multimodal optimization''' deals with [[Mathematical optimization|optimization]] tasks that involve finding all or most of the multiple solutions (asat opposedleast tolocally aoptimal) singlesolutions best solution) toof a problem.<ref>Wong, K.as C.opposed (2015),to [http://arxiv.org/abs/1508.00457a Evolutionarysingle Multimodalbest Optimization: A Short Survey] arXiv preprint arXiv:1508solution.00457</ref>

Evolutionary multimodal optimization is a branch of [[Artificial intelligence|Evolutionary computation]], which is closely related to [[Computer science|Machine learning]]. Wong provides a short survey<ref>Wong, K. C. (2015), [http://arxiv.org/abs/1508.00457 Evolutionary Multimodal Optimization: A Short Survey] arXiv preprint arXiv:1508.00457</ref>, the book of Preuss<ref>Preuss, Mike (2015)[http://www.springer.com/de/book/9783319074061 Evolutionary Multimodal Optimization]</ref> covers the topic in more detail.

== 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 (locallocally and/or globalglobally optimal) are known, the implementation can be quickly switched to another solution and still obtain anthe optimalbest possible system performance. Multiple solutions could also be analyzed to discover hidden properties (or relationships) of the underlying optimization problem, which makes them high-performingimportant 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), [http://dx.doi.org/10.1016/j.ins.2011.12.016 Evolutionary multimodal optimization using the principle of locality] Information Sciences</ref>