Year: 2025
Type: article
Abstract: The sensor coverage problem is a well-known combinatorial optimization problem that continues to attract the attention of many researchers. The existing game-based algorithms mainly pursue a feasible solution when solving this problem. This problem is described as a potential game, and a memory-based greedy learning (MGL) algorithm is proposed, which can ensure convergence to Nash equilibrium. Compared with existing representative algorithms, our proposed algorithm performs the best in terms of average coverage, best value, and standard deviation within within a suitable time. In addition, increasing memory length helps to generate a better Nash equilibrium.
Source: Mathematics
DOI: 10.3390/math13172709
Authors: Jun Huang, Jie Chen, Rongcheng Dong, Xinli Xiong, Simao Xu
Institution: National University of Defense Technology
Language: English
Topic: Game Theory and Applications
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4413413653