A coalition-structure's generation method for solving cooperative computing problems in edge computing environments

Abstract Coalition-structure’s generation methods are usually employed to solve team allocation optimization problems or cooperative computing scheduling problems in the case of multitasking concurrency. In edge computing environments, affected by such factors as a large number of edge nodes, weak computing power, multiple optimization objectives and multiple constraints, the traditional methods can hardly guarantee the optimization speed and the optimal solution’s quality when solving similar problems. Based on the advantages of cooperative game algorithms and heuristic algorithms, we propose a coalition-structure’s generation method suitable for edge computing environments in this paper. Firstly, we introduce the concept of bargaining set and remove the impossible coalition-structures by judging the no-bargain coalition to narrow the strategic space. Secondly, for increasing the optimization speed and the optimal solution’s quality, we improve the inertia weight computing method and the particle state determination method of the primary discrete particle swarm, propose M-ary discrete particle swarm optimization (MDPSO). Finally, we design a series of contrast experiments and verify that this method boasts obvious advantages in optimization speed, the optimal solution’s quality, stability, and other aspects.