Solving Sequential Competitive Facility Location Challenges: Using Parallel Genetic Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In smart computing, addressing combinatorial optimization game theory problems is essential for resolving intricate challenges involving limited resources and significant strategic interactions among decision-makers. In this paper, we solved sequential combinatorial optimization game theory problems using an evolutionary algorithm called the Game-Theoretic Genetic Algorithm (GTGA), where it is computationally infeasible to enumerate all players' decision options. The computational experiments show that the proposed fitness evaluation methods enable the GTGA to converge to true Stackelberg equilibria. Our research highlights the practical applicability of the GTGA in addressing real-world problems characterized by dynamic decision-making and strategic interactions.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE International Conference on Smart Computing, SMARTCOMP 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages234-236
Number of pages3
ISBN (Electronic)9798350349948
DOIs
StatePublished - 2024
Event10th IEEE International Conference on Smart Computing, SMARTCOMP 2024 - Osaka, Japan
Duration: Jun 29 2024Jul 2 2024

Publication series

NameProceedings - 2024 IEEE International Conference on Smart Computing, SMARTCOMP 2024

Conference

Conference10th IEEE International Conference on Smart Computing, SMARTCOMP 2024
Country/TerritoryJapan
CityOsaka
Period6/29/247/2/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Optimization

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