Coalitions are collections of agents that join together to solve a common problem that either cannot be solved individually or can be solved more efficiently as a group. Each individual agent has capabilities that can benefit the group when working together as a coalition. Typically, individual capabilities are joined together in an additive way when forming a coalition. This work will introduce a new operator that is used when combining capabilities, and suggest that the behavior of the operator is contextual, depending on the nature of the capability itself. This work considers six different capabilities of Unmanned Air Vehicles (UAV) and determines the nature of the new operator in the context of each capability as coalitions (squadrons) of UAVs are formed. Coalitions are formed using three different search algorithms, both with and without heuristics: Depth-First, Depth-First Iterative Deepening, and Genetic Algorithm (GA). The effectiveness of each algorithm is evaluated. Multi agent-based UAV simulation software was developed and used to test the ideas presented. In addition to coalition formation, the software aims to address additional multi-agent issues such as agent identity, mutability, and communication as applied to UAV systems, in a realistic simulated environment. Social potential fields provide a means of modeling a clustering attractive force at the same time as a collision-avoiding repulsive force, and are used by the simulation to maintain aircraft position relative to other UAVs.