99,614 research outputs found
Convergence Analysis of Mixed Timescale Cross-Layer Stochastic Optimization
This paper considers a cross-layer optimization problem driven by
multi-timescale stochastic exogenous processes in wireless communication
networks. Due to the hierarchical information structure in a wireless network,
a mixed timescale stochastic iterative algorithm is proposed to track the
time-varying optimal solution of the cross-layer optimization problem, where
the variables are partitioned into short-term controls updated in a faster
timescale, and long-term controls updated in a slower timescale. We focus on
establishing a convergence analysis framework for such multi-timescale
algorithms, which is difficult due to the timescale separation of the algorithm
and the time-varying nature of the exogenous processes. To cope with this
challenge, we model the algorithm dynamics using stochastic differential
equations (SDEs) and show that the study of the algorithm convergence is
equivalent to the study of the stochastic stability of a virtual stochastic
dynamic system (VSDS). Leveraging the techniques of Lyapunov stability, we
derive a sufficient condition for the algorithm stability and a tracking error
bound in terms of the parameters of the multi-timescale exogenous processes.
Based on these results, an adaptive compensation algorithm is proposed to
enhance the tracking performance. Finally, we illustrate the framework by an
application example in wireless heterogeneous network
A general framework of multi-population methods with clustering in undetectable dynamic environments
Copyright @ 2011 IEEETo solve dynamic optimization problems, multiple
population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g.,
how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this
paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental
results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks
benchmark
Effective Target Aware Visual Navigation for UAVs
In this paper we propose an effective vision-based navigation method that
allows a multirotor vehicle to simultaneously reach a desired goal pose in the
environment while constantly facing a target object or landmark. Standard
techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual
Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast
maneuvers) do not allow to constantly maintain the line of sight with a target
of interest. Instead, we compute the optimal trajectory by solving a non-linear
optimization problem that minimizes the target re-projection error while
meeting the UAV's dynamic constraints. The desired trajectory is then tracked
by means of a real-time Non-linear Model Predictive Controller (NMPC): this
implicitly allows the multirotor to satisfy both the required constraints. We
successfully evaluate the proposed approach in many real and simulated
experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR)
201
FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation
One of the most popular approaches to multi-target tracking is
tracking-by-detection. Current min-cost flow algorithms which solve the data
association problem optimally have three main drawbacks: they are
computationally expensive, they assume that the whole video is given as a
batch, and they scale badly in memory and computation with the length of the
video sequence. In this paper, we address each of these issues, resulting in a
computationally and memory-bounded solution. First, we introduce a dynamic
version of the successive shortest-path algorithm which solves the data
association problem optimally while reusing computation, resulting in
significantly faster inference than standard solvers. Second, we address the
optimal solution to the data association problem when dealing with an incoming
stream of data (i.e., online setting). Finally, we present our main
contribution which is an approximate online solution with bounded memory and
computation which is capable of handling videos of arbitrarily length while
performing tracking in real time. We demonstrate the effectiveness of our
algorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art
performance, while being significantly faster than existing solvers
A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
A distributed model predictive control (DMPC) approach based on distributed
optimization is applied to the power reference tracking problem of a hydro
power valley (HPV) system. The applied optimization algorithm is based on
accelerated gradient methods and achieves a convergence rate of O(1/k^2), where
k is the iteration number. Major challenges in the control of the HPV include a
nonlinear and large-scale model, nonsmoothness in the power-production
functions, and a globally coupled cost function that prevents distributed
schemes to be applied directly. We propose a linearization and approximation
approach that accommodates the proposed the DMPC framework and provides very
similar performance compared to a centralized solution in simulations. The
provided numerical studies also suggest that for the sparsely interconnected
system at hand, the distributed algorithm we propose is faster than a
centralized state-of-the-art solver such as CPLEX
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