Achieving Social Optimum in Non-convex Cooperative Aggregative Games: A Distributed Stochastic Annealing Approach

Abstract

This paper designs a distributed stochastic annealing algorithm for non-convex cooperative aggregative games, whose agents' cost functions not only depend on agents' own decision variables but also rely on the sum of agents' decision variables. To seek the the social optimum of cooperative aggregative games, a distributed stochastic annealing algorithm is proposed, where the local cost functions are non-convex and the communication topology between agents is time varying. The weak convergence to the social optimum of the algorithm is further analyzed. A numerical example is given to illustrate the effectiveness of the proposed algorithm.

Leader-follower security games in UAV communication with deception

“…Each UAV always hopes to maximize its own utility. There are also some other practical communication networks [16][17][18][19][20] , while we consider a star communication network. Actually, as a key property of UAV security problems, we always suppose that 𝐶 𝑙 (𝑡 𝑘 ) > 𝑄 𝑙 (𝑡 𝑘 ), for all 𝑘 = 1, … , 𝐾.…”

Efficient deployment in UAV from the perspective of distributed potential games

“…As game theory involves the interaction behaviors of different decision makers [5][6][7] , employing game models to discuss interactive mechanism between UAVs have attracted extensive research. One of the natural representative solution concepts in game theory is Nash equilibrium (NE), which characterizes the set of stable points that rational players would decide not to deviate from [8][9][10] .…”