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      Escaping local optima in a class of multi-agent distributed optimization problems: a boosting function approach

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      Author
      Sun, X.
      Cassandras, C. G.
      Gökbayrak, Kaan
      Date
      2014
      Source Title
      Proceedings of the 53rd IEEE Conference on Decision and Control, IEEE 2014
      Print ISSN
      0743-1546
      Publisher
      IEEE
      Pages
      3701 - 3706
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      We address the problem of multiple local optima commonly arising in optimization problems for multi-agent systems, where objective functions are nonlinear and nonconvex. For the class of coverage control problems, we propose a systematic approach for escaping a local optimum, rather than randomly perturbing controllable variables away from it. We show that the objective function for these problems can be decomposed to facilitate the evaluation of the local partial derivative of each node in the system and to provide insights into its structure. This structure is exploited by defining 'boosting functions' applied to the aforementioned local partial derivative at an equilibrium point where its value is zero so as to transform it in a way that induces nodes to explore poorly covered areas of the mission space until a new equilibrium point is reached. The proposed boosting process ensures that, at its conclusion, the objective function is no worse than its pre-boosting value. However, the global optima cannot be guaranteed. We define three families of boosting functions with different properties and provide simulation results illustrating how this approach improves the solutions obtained for this class of distributed optimization problems.
      Keywords
      Boosting
      Linear programming
      Space missions
      Optimization
      Sensors
      Aerospace electronics
      Permalink
      http://hdl.handle.net/11693/27241
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/CDC.2014.7039965
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      • Department of Industrial Engineering 677
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