Traditionally, complex systems were treated using centralized approaches; however, recent trends highlighted that the growing complexity can be best dealt by decomposing the entire system into subsystems and further treat them in a distributed way. The approach of Probability Collectives (PC) in Collective Intelligence (COIN) framework decomposes the entire system into a Multi-Agent System (MAS) or a collection of rational and self interested agents and further optimizes them in a distributed and decentralized way to reach the desired system objective. The complexity of the system increases when constraints are involved. The approach of PC is incorporated with a feasibility-based rule to handle the solution based on number of constraints violated, and further drives the convergence towards feasibility. Importantly for the first time, constrained PC approach has been tested solving a discrete problem such as 45-bar truss structure. The results are validated by comparing with the contemporary algorithms as well.