13,011 research outputs found
Design of Hybrid Regrouping PSO-GA based Sub-optimal Networked Control System with Random Packet Losses
In this paper, a new approach has been presented to design sub-optimal state
feedback regulators over Networked Control Systems (NCS) with random packet
losses. The optimal regulator gains, producing guaranteed stability are
designed with the nominal discrete time model of a plant using Lyapunov
technique which produces a few set of Bilinear Matrix Inequalities (BMIs). In
order to reduce the computational complexity of the BMIs, a Genetic Algorithm
(GA) based approach coupled with the standard interior point methods for LMIs
has been adopted. A Regrouping Particle Swarm Optimization (RegPSO) based
method is then employed to optimally choose the weighting matrices for the
state feedback regulator design that gets passed through the GA based stability
checking criteria i.e. the BMIs. This hybrid optimization methodology put
forward in this paper not only reduces the computational difficulty of the
feasibility checking condition for optimum stabilizing gain selection but also
minimizes other time domain performance criteria like expected value of the
set-point tracking error with optimum weight selection based LQR design for the
nominal system.Comment: 27 pages, 7 figure
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms
Bi-level optimisation problems have gained increasing interest in the field
of combinatorial optimisation in recent years. With this paper, we start the
runtime analysis of evolutionary algorithms for bi-level optimisation problems.
We examine two NP-hard problems, the generalised minimum spanning tree problem
(GMST), and the generalised travelling salesman problem (GTSP) in the context
of parameterised complexity.
For the generalised minimum spanning tree problem, we analyse the two
approaches presented by Hu and Raidl (2012) with respect to the number of
clusters that distinguish each other by the chosen representation of possible
solutions. Our results show that a (1+1) EA working with the spanning nodes
representation is not a fixed-parameter evolutionary algorithm for the problem,
whereas the global structure representation enables to solve the problem in
fixed-parameter time. We present hard instances for each approach and show that
the two approaches are highly complementary by proving that they solve each
other's hard instances very efficiently.
For the generalised travelling salesman problem, we analyse the problem with
respect to the number of clusters in the problem instance. Our results show
that a (1+1) EA working with the global structure representation is a
fixed-parameter evolutionary algorithm for the problem
Wireless MIMO Switching with Zero-forcing Relaying and Network-coded Relaying
A wireless relay with multiple antennas is called a
multiple-input-multiple-output (MIMO) switch if it maps its input links to its
output links using "precode-and-forward." Namely, the MIMO switch precodes the
received signal vector in the uplink using some matrix for transmission in the
downlink. This paper studies the scenario of stations and a MIMO switch,
which has full channel state information. The precoder at the MIMO switch is
either a zero-forcing matrix or a network-coded matrix. With the zero-forcing
precoder, each destination station receives only its desired signal with
enhanced noise but no interference. With the network-coded precoder, each
station receives not only its desired signal and noise, but possibly also
self-interference, which can be perfectly canceled. Precoder design for
optimizing the received signal-to-noise ratios at the destinations is
investigated. For zero-forcing relaying, the problem is solved in closed form
in the two-user case, whereas in the case of more users, efficient algorithms
are proposed and shown to be close to what can be achieved by extensive random
search. For network-coded relaying, we present efficient iterative algorithms
that can boost the throughput further.Comment: This version is to appear in IEEE Journal on Selected Areas in
Communications later in 201
Better Algorithms for Hybrid Circuit and Packet Switching in Data Centers
Hybrid circuit and packet switching for data center networking (DCN) has
received considerable research attention recently. A hybrid-switched DCN
employs a much faster circuit switch that is reconfigurable with a nontrivial
cost, and a much slower packet switch that is reconfigurable with no cost, to
interconnect its racks of servers. The research problem is, given a traffic
demand matrix (between the racks), how to compute a good circuit switch
configuration schedule so that the vast majority of the traffic demand is
removed by the circuit switch, leaving a remaining demand matrix that contains
only small elements for the packet switch to handle. In this paper, we propose
two new hybrid switch scheduling algorithms under two different scheduling
constraints. Our first algorithm, called 2-hop Eclipse, strikes a much better
tradeoff between the resulting performance (of the hybrid switch) and the
computational complexity (of the algorithm) than the state of the art solution
Eclipse/Eclipse++. Our second algorithm, called BFF (best first fit), is the
first hybrid switching solution that exploits the potential partial
reconfiguration capability of the circuit switch for performance gains
Household Electricity Consumption Data Cleansing
Load curve data in power systems refers to users' electrical energy
consumption data periodically collected with meters. It has become one of the
most important assets for modern power systems. Many operational decisions are
made based on the information discovered in the data. Load curve data, however,
usually suffers from corruptions caused by various factors, such as data
transmission errors or malfunctioning meters. To solve the problem, tremendous
research efforts have been made on load curve data cleansing. Most existing
approaches apply outlier detection methods from the supply side (i.e.,
electricity service providers), which may only have aggregated load data. In
this paper, we propose to seek aid from the demand side (i.e., electricity
service users). With the help of readily available knowledge on consumers'
appliances, we present a new appliance-driven approach to load curve data
cleansing. This approach utilizes data generation rules and a Sequential Local
Optimization Algorithm (SLOA) to solve the Corrupted Data Identification
Problem (CDIP). We evaluate the performance of SLOA with real-world trace data
and synthetic data. The results indicate that, comparing to existing load data
cleansing methods, such as B-spline smoothing, our approach has an overall
better performance and can effectively identify consecutive corrupted data.
Experimental results also demonstrate that our method is robust in various
tests. Our method provides a highly feasible and reliable solution to an
emerging industry application.Comment: 12 pages, 12 figures; update: modified title and introduction, and
corrected some typo
On the Problem of Optimal Path Encoding for Software-Defined Networks
Packet networks need to maintain state in the form of forwarding tables at
each switch. The cost of this state increases as networks support ever more
sophisticated per-flow routing, traffic engineering, and service chaining.
Per-flow or per-path state at the switches can be eliminated by encoding each
packet's desired path in its header. A key component of such a method is an
efficient encoding of paths through the network. We introduce a mathematical
formulation of this optimal path-encoding problem. We prove that the problem is
APX-hard, by showing that approximating it to within a factor less than 8/7 is
NP-hard. Thus, at best we can hope for a constant-factor approximation
algorithm. We then present such an algorithm, approximating the optimal
path-encoding problem to within a factor 2. Finally, we provide empirical
results illustrating the effectiveness of the proposed algorithm.Comment: To appear in IEEE/ACM Transactions on Networkin
Energy efficient D2D communications in dynamic TDD systems
Network-assisted device-to-device communication is a promising technology for
improving the performance of proximity-based services. This paper demonstrates
how the integration of device-to-device communications and dynamic
time-division duplex can improve the energy efficiency of future cellular
networks, leading to a greener system operation and a prolonged battery
lifetime of mobile devices. We jointly optimize the mode selection,
transmission period and power allocation to minimize the energy consumption
(from both a system and a device perspective) while satisfying a certain rate
requirement. The radio resource management problems are formulated as
mixed-integer nonlinear programming problems. Although they are known to be
NP-hard in general, we exploit the problem structure to design efficient
algorithms that optimally solve several problem cases. For the remaining cases,
a heuristic algorithm that computes near-optimal solutions while respecting
practical constraints on execution times and signaling overhead is also
proposed. Simulation results confirm that the combination of device-to-device
and flexible time-division-duplex technologies can significantly enhance
spectrum and energy-efficiency of next generation cellular systems.Comment: Submitted to IEEE Journal of Selected Areas in Communication
Experimental Design for Cost-Aware Learning of Causal Graphs
We consider the minimum cost intervention design problem: Given the essential
graph of a causal graph and a cost to intervene on a variable, identify the set
of interventions with minimum total cost that can learn any causal graph with
the given essential graph. We first show that this problem is NP-hard. We then
prove that we can achieve a constant factor approximation to this problem with
a greedy algorithm. We then constrain the sparsity of each intervention. We
develop an algorithm that returns an intervention design that is nearly optimal
in terms of size for sparse graphs with sparse interventions and we discuss how
to use it when there are costs on the vertices.Comment: In NIPS 201
Power-Aware Virtual Network Function Placement and Routing using an Abstraction Technique
The Network Function Virtualization (NFV) is very promising for efficient
provisioning of network services and is attracting a lot of attention. NFV can
be implemented in commercial off-the-shelf servers or Physical Machines (PMs),
and many network services can be offered as a sequence of Virtual Network
Functions (VNFs), known as VNF chains. Furthermore, many existing network
devices (e.g., switches) and collocated PMs are underutilized or
over-provisioned, resulting in low power-efficiency. In order to achieve more
energy efficient systems, this work aims at designing the placement of VNFs
such that the total power consumption in network nodes and PMs is minimized,
while meeting the delay and capacity requirements of the foreseen demands.
Based on existing switch and PM power models, we propose a Integer Linear
Programming (ILP) formulation to find the optimal solution. We also propose a
heuristic based on the concept of Blocking Islands (BI), and a baseline
heuristic based on the Betweenness Centrality (BC) property of the graph. Both
heuristics and the ILP solutions have been compared in terms of total power
consumption, delay, demands acceptance rate, and computation time. Our
simulation results suggest that BI-based heuristic is superior compared with
the BC-based heuristic, and very close to the optimal solution obtained from
the ILP in terms of total power consumption and demands acceptance rate.
Compared to the ILP, the proposed BI-based heuristic is significantly faster
and results in 22% lower end-to-end delay, with a penalty of consuming 6% more
power in average.Comment: IEEE Global Communications Conference (GLOBECOM) 2018, Abu Dhabi, UA
How Hard Is Bribery in Elections?
We study the complexity of influencing elections through bribery: How
computationally complex is it for an external actor to determine whether by a
certain amount of bribing voters a specified candidate can be made the
election's winner? We study this problem for election systems as varied as
scoring protocols and Dodgson voting, and in a variety of settings regarding
homogeneous-vs.-nonhomogeneous electorate bribability,
bounded-size-vs.-arbitrary-sized candidate sets, weighted-vs.-unweighted
voters, and succinct-vs.-nonsuccinct input specification. We obtain both
polynomial-time bribery algorithms and proofs of the intractability of bribery,
and indeed our results show that the complexity of bribery is extremely
sensitive to the setting. For example, we find settings in which bribery is
NP-complete but manipulation (by voters) is in P, and we find settings in which
bribing weighted voters is NP-complete but bribing voters with individual bribe
thresholds is in P. For the broad class of elections (including plurality,
Borda, k-approval, and veto) known as scoring protocols, we prove a dichotomy
result for bribery of weighted voters: We find a simple-to-evaluate condition
that classifies every case as either NP-complete or in P.Comment: Earlier version appears in Proc. of AAAI-06, pp. 641-646, 200
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