Distributed Satellite Collection Scheduling optimization using Cooperative Coevolution and Market-Based Techniques

In this paper, we propose and adapt decision model formulations and algorithms suitable to the distributed satellite collection tasking problem. The decentralized multi-satellite scheduling problem setting comprises multiple stakeholders having control on their own resources to be coordinated in a time-constrained uncertain environment. Aimed at maximizing global (system-wide) and local collection value objectives, satellite platform agents are assumed to have sufficient on-board processing and decision-making capability. Agent's attitude may be defined over a mixed spectrum of cooperative goal-based behaviors. Two novel collection tasking coordination approaches relying on market-based and cooperative co-evolution mechanisms are introduced. A basic description is given for both approaches while depicting how competitive, cooperative and mixed agent attitudes are handled. Computational results reporting comparative performance show the value of the proposed solutions.