15,518 research outputs found
Searching for quantum optimal controls under severe constraints
The success of quantum optimal control for both experimental and theoretical
objectives is connected to the topology of the corresponding control
landscapes, which are free from local traps if three conditions are met: (1)
the quantum system is controllable, (2) the Jacobian of the map from the
control field to the evolution operator is of full rank, and (3) there are no
constraints on the control field. This paper investigates how the violation of
assumption (3) affects gradient searches for globally optimal control fields.
The satisfaction of assumptions (1) and (2) ensures that the control landscape
lacks fundamental traps, but certain control constraints can still introduce
artificial traps. Proper management of these constraints is an issue of great
practical importance for numerical simulations as well as optimization in the
laboratory. Using optimal control simulations, we show that constraints on
quantities such as the number of control variables, the control duration, and
the field strength are potentially severe enough to prevent successful
optimization of the objective. For each such constraint, we show that exceeding
quantifiable limits can prevent gradient searches from reaching a globally
optimal solution. These results demonstrate that careful choice of relevant
control parameters helps to eliminate artificial traps and facilitate
successful optimization.Comment: 16 pages, 7 figure
Moving Walkways, Escalators, and Elevators
We study a simple geometric model of transportation facility that consists of
two points between which the travel speed is high. This elementary definition
can model shuttle services, tunnels, bridges, teleportation devices, escalators
or moving walkways. The travel time between a pair of points is defined as a
time distance, in such a way that a customer uses the transportation facility
only if it is helpful.
We give algorithms for finding the optimal location of such a transportation
facility, where optimality is defined with respect to the maximum travel time
between two points in a given set.Comment: 16 pages. Presented at XII Encuentros de Geometria Computacional,
Valladolid, Spai
Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks
This article is posted here with permission of IEEE - Copyright @ 2010 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.This work was supported by the Engineering
and Physical Sciences Research Council of U.K. underGrant EP/E060722/
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