Bayesian-DPOP for continuous distributed constraint optimization problems

Conference paper (2019)

Authors

J.E. Fransman Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
J. Sijs TNO
Henry Dol TNO
Erik Theunissen Netherlands Defense Academy (NLDA)
B.H.K. De Schutter Delft Center for Systems and Control , Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
Bayesian optimization DCOP DPOP Distributed optimization
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Published Date

2019

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

Abstract

In this work, the novel algorithm Bayesian Dynamic Programming Optimization Procedure (B-DPOP) is presented to solve multi-agent problems within the Distributed Constraint Optimization Problem framework. The Bayesian optimization framework is used to prove convergence to the global optimum of the B-DPOP algorithm for Lipschitz-continuous objective functions. The proposed algorithm is assessed based on the benchmark problem known as dynamic sensor placement. Results show increased performance over related algorithms in terms of sample-efficiency.

Files

P1961.pdf
(.pdf | 1.53 Mb)