A modified feasibility-based rule for solving constrained optimization problems using Probability Collectives

The complex systems can be best dealt by decomposing them into subsystems or Multi-Agent System (MAS) and further treat them in a distributed way. However, coordinating these agents to achieve the best possible global objective is one of the challenging issues. The problem becomes harder when the constraints are involved. This paper proposes the approach of Probability Collectives (PC) in the Collective Intelligence (COIN) framework for modeling and controlling the distributed MAS. At the core of the PC methodology are the Deterministic Annealing and Game Theory. In order to make it more generic and capable of handling constraints, feasibility-based rule is incorporated to handle solutions based on the number of constraints violated and drive the convergence towards feasibility. The approach is validated by successfully solving two test problems. The proposed algorithm is shown to be sufficiently robust and other strengths, weaknesses and future directions are discussed.

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