Evolutionary optimization of large complex problems Article Swipe

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Handing Wang , Chaoli Sun , Jinliang Ding , Yew-Soon Ong ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1007/s40747-022-00766-x

Many real-world applications, such as industrial manufacturing systems and water distribution networks, are complex systems, which may be hard to describe with explicit mathematical models.These are commonly labeled as black-box problems.Due to the increasing complexities of today's fast changing environment, the demands for solving problems without any explicitly defined mathematical functions have dramatically increased.Traditional mathematical solvers, for example, the gradient descent method and the quasi-Newton methods, cannot be easily used to solve complex blackbox optimization problems without gradient information.Evolutionary optimization as such provides the appropriate gradient-free alternative for solving black-box problems.However, with the rapid development of complex systems, the optimization problems become much larger.For example, the dimension of objective functions, decision variables or constraints is high, which poses challenges to existing evolutionary algorithms.This special issue has attracted researchers to report the newest research on the development and applications in the field of evolutionary optimization for large complex problems.

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Type: article
Language: en
Landing Page: https://doi.org/10.1007/s40747-022-00766-x
PDF: https://link.springer.com/content/pdf/10.1007/s40747-022-00766-x.pdf
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Cited By: 2
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DOI: https://doi.org/10.1007/s40747-022-00766-x

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Title: Evolutionary optimization of large complex problems

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Language: en

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Publication year: 2022

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Publication date: 2022-05-10

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Authors: Handing Wang, Chaoli Sun, Jinliang Ding, Yew-Soon Ong

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Landing page: https://doi.org/10.1007/s40747-022-00766-x

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Concepts: Computational intelligence, Computer science, Artificial intelligence

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Cited by: 2

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