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Solving Multiobjective Game in Multiconflict Situation Based on Adaptive Differential Evolution Algorithm with Simulated Annealing

Author

Listed:
  • Huimin Li
  • Shuwen Xiang
  • Wensheng Jia
  • Yanlong Yang
  • Shiguo Huang

Abstract

In this paper, we study the multiobjective game in a multiconflict situation. First, the feasible strategy set and synthetic strategy space are constructed in the multiconflict situation. Meanwhile, the value of payoff function under multiobjective is determined, and an integrated multiobjective game model is established in a multiconflict situation. Second, the multiobjective game model is transformed into the single-objective game model by the Entropy Weight Method. Then, in order to solve this multiobjective game, an adaptive differential evolution algorithm based on simulated annealing (ADESA) is proposed to solve this game, which is to improve the mutation factor and crossover operator of the differential evolution (DE) algorithm adaptively, and the Metropolis rule with probability mutation ability of the simulated annealing (SA) algorithm is used. Finally, the practicability and effectiveness of the algorithm are illustrated by a military example.

Suggested Citation

  • Huimin Li & Shuwen Xiang & Wensheng Jia & Yanlong Yang & Shiguo Huang, 2021. "Solving Multiobjective Game in Multiconflict Situation Based on Adaptive Differential Evolution Algorithm with Simulated Annealing," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, July.
    • Handle: RePEc:hin:jnlmpe:9957279
    DOI: 10.1155/2021/9957279
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