Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85294
Title: A modified feasibility-based rule for solving constrained optimization problems using probability collectives
Authors: Kulkarni, Anand J.
Patankar, N.S.
Sandupatla, Amani.
Tai, K.
Issue Date: 2012
Source: Kulkarni, A. J., Patankar, N., Sandupatla, A.,& Tai, K. (2012). A modified feasibility-based rule for solving constrained optimization problems using Probability Collectives. 2012 12th International Conference on Hybrid Intelligent Systems (HIS), 213 - 218.
Abstract: 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.
URI: https://hdl.handle.net/10356/85294
http://hdl.handle.net/10220/12774
DOI: 10.1109/HIS.2012.6421336
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Conference Papers

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