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Static and Dynamic Optimization Prob...

Sun, Xinmiao.

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Static and Dynamic Optimization Problems in Cooperative Multi-Agent Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Static and Dynamic Optimization Problems in Cooperative Multi-Agent Systems./
作者:	Sun, Xinmiao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	137 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
Contained By:	Dissertation Abstracts International79-02B(E).
標題:	Systems science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10620008
ISBN:	9780355446449

Static and Dynamic Optimization Problems in Cooperative Multi-Agent Systems.
Sun, Xinmiao.

Static and Dynamic Optimization Problems in Cooperative Multi-Agent Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 137 p.

Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.

Thesis (Ph.D.)--Boston University, 2017.

This dissertation focuses on challenging static and dynamic problems encountered in cooperative multi-agent systems. First, a unified optimization framework is proposed for a wide range of tasks including consensus, optimal coverage, and resource allocation problems. It allows gradient-based algorithms to be applied to solve these problems, all of which have been studied in a separate way in the past. Gradient-based algorithms are shown to be distributed for a subclass of problems where objective functions can be decoupled.

ISBN: 9780355446449Subjects--Topical Terms:

3168411
Systems science.

Static and Dynamic Optimization Problems in Cooperative Multi-Agent Systems.
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500 $a Source: Dissertation Abstracts International, Volume: 79-02(E), Section: B.
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520 $a This dissertation focuses on challenging static and dynamic problems encountered in cooperative multi-agent systems. First, a unified optimization framework is proposed for a wide range of tasks including consensus, optimal coverage, and resource allocation problems. It allows gradient-based algorithms to be applied to solve these problems, all of which have been studied in a separate way in the past. Gradient-based algorithms are shown to be distributed for a subclass of problems where objective functions can be decoupled.
520 $a Second, the issue of global optimality is studied for optimal coverage problems where agents are deployed to maximize the joint detection probability. Objective functions in these problems are non-convex and no global optimum can be guaranteed by gradient-based algorithms developed to date. In order to obtain a solution close to the global optimum, the selection of initial conditions is crucial. The initial state is determined by an additional optimization problem where the objective function is monotone submodular, a class of functions for which the greedy solution performance is guaranteed to be within a provable bound relative to the optimal performance. The bound is known to be within 1 -- 1/e of the optimal solution and is improved by exploiting the curvature information of the objective function. The greedy solution is subsequently used as an initial point of a gradient-based algorithm for the original optimal coverage problem. In addition, a novel method is proposed to escape a local optimum in a systematic way instead of randomly perturbing controllable variables away from a local optimum.
520 $a Finally, optimal dynamic formation control problems are addressed for mobile leader-follower networks. Optimal formations are determined by maximizing a given objective function while continuously preserving communication connectivity in a time-varying environment. It is shown that in a convex mission space, the connectivity constraints can be satisfied by any feasible solution to a Mixed Integer Nonlinear Programming (MINLP) problem. For the class of optimal formation problems where the objective is to maximize coverage, the optimal formation is proven to be a tree which can be efficiently constructed without solving a MINLP problem. In a mission space constrained by obstacles, a minimum-effort reconfiguration approach is designed for obtaining the formation which still optimizes the objective function while avoiding the obstacles and ensuring connectivity.
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