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Biologically Inspired Techniques in Many-Criteria Decision Making
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Biologically Inspired Techniques in Many-Criteria Decision Making : International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019)

2020 ed.

Book Details

Format Paperback / Softback
ISBN-10 3030390357
ISBN-13 9783030390358
Edition 2020 ed.
Publisher Springer Nature Switzerland AG
Imprint Springer Nature Switzerland AG
Country of Manufacture GB
Country of Publication GB
Publication Date Jan 22nd, 2021
Print length 258 Pages
Ksh 32,400.00
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This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems.

This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems.

This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing. 


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