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Updated: Oct 12, 2025

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Game theory and evolutionary optimization approaches applied to resource allocation problems in computing

Shahab Shamshirband1, Javad Hassannataj Joloudari2,3, Sahar Khanjani Shirkharkolaie3

  • 1Future Technology Research Center, College of Future, National Yunlin University of Science and Technology 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan.

Mathematical Biosciences and Engineering : MBE
|November 24, 2021
PubMed
Summary
This summary is machine-generated.

Intelligent computing environments like IoT, Cloud, Fog, and Edge computing require advanced resource allocation. This research surveys computational intelligence and game theory for optimized resource management in these dynamic systems.

Keywords:
Internet of thingscloud computingevolutionary optimization methodsfog computinggame theory modelsresource allocation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Distributed Computing

Background:

  • Intelligent computing environments like Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC) are crucial for optimizing resource allocation, quality of service, and energy consumption.
  • Existing resource allocation methods are insufficient for complex, dynamic systems with real-time data demands and fierce competition.
  • There is a need for robust and reliable resource allocation capabilities in integrated computing environments.

Purpose of the Study:

  • To conduct a comprehensive survey of resource allocation problems in modern computing architectures.
  • To explore the application of computational intelligence-based evolutionary optimization and mathematical game theory in resource allocation.
  • To identify the latest scientific research achievements in optimizing CC/FC/EC/IoT environments.

Main Methods:

  • Literature review of scientific research achievements in resource allocation.
  • Analysis of computational intelligence techniques, including evolutionary optimization.
  • Examination of mathematical game theory approaches for resource allocation.

Main Results:

  • Traditional resource allocation methods are inadequate for dynamic, large-scale computing environments.
  • Computational intelligence and game theory offer promising solutions for optimizing resource allocation.
  • Intelligent design of CC/FC/EC/IoT architectures is essential for efficient resource management.

Conclusions:

  • Advanced computational intelligence and game theory are vital for effective resource allocation in complex computing environments.
  • Optimized resource allocation aims to minimize delay, energy consumption, and computational complexity while maximizing scalability and utilization.
  • This survey provides insights into the latest research for developing intelligent, adaptive resource management strategies.