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An Optimization Model Based on Game Theory

Authors
Publisher
ACADEMY PUBLISHER
Publication Date
Keywords
  • Game Theory
  • Artificial Intelligence
  • Learning Model
  • Nash Equilibrium
Disciplines
  • Computer Science
  • Economics
  • Mathematics

Abstract

Game Theory has a wide range of applications in department of economics, but in the field of computer science, especially in the optimization algorithm is seldom used. In this paper, we integrate thinking of game theory into optimization algorithm, and then propose a new optimization model which can be widely used in optimization processing. This optimization model is divided into two types, which are called “the complete consistency” and “the partial consistency”. In these two types, the partial consistency is added disturbance strategy on the basis of the complete consistency. When model’s consistency is satisfied, the Nash equilibrium of the optimization model is global optimal and when the model’s consistency is not met, the presence of perturbation strategy can improve the application of the algorithm. The basic experiments suggest that this optimization model has broad applicability and better performance, and gives a new idea for some intractable problems in the field of artificial intelligence

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