Overview of the application of intelligent optimization algorithms in multi-attribute group decision making Overview of the application of intelligent optimization algorithms in multi-attribute group decision making

Intelligent Optimization Algorithms (IOAs) have great potential in solving multi-attribute group decision-making (MAGDM) problems. These problems have gradually become a research hotspot in the field of intelligent decision-making due to their advantages of high decision-making accuracy, versatility...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 55; H. 6; S. 409
Hauptverfasser: Kang, Kaiying, Xie, Jialiang, Liu, Xiaohui, Wang, Honghui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2025
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computer Science
Decision making
Feasibility studies
Feedback
Fuzzy sets
Keywords
Linguistics
Machines
Manufacturing
Mechanical Engineering
Optimization
Optimization algorithms
Preferences
Processes
Semantics
IOAs
Overview
Fuzzy theory
MAGDM
ISSN:0924-669X, 1573-7497
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Zusammenfassung:Intelligent Optimization Algorithms (IOAs) have great potential in solving multi-attribute group decision-making (MAGDM) problems. These problems have gradually become a research hotspot in the field of intelligent decision-making due to their advantages of high decision-making accuracy, versatility, and objective evaluation. This study provides a detailed analysis of the challenges in the MAGDM process and evaluates the feasibility of applying IOAs in this context. Specifically, we study the application of IOAs in the MAGDM process and discuss the advantages and limitations across various application scenarios, including the applications of granulating linguistic information, adjusting decision information, optimizing weights, and aggregating decision information. In addition, the development prospects and challenges of IOAs integration with MAGDM are summarized.
Bibliographie:ObjectType-Article-1
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-025-06324-5