8,333 research outputs found
Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)
We explore the use of the Cell Broadband Engine (Cell/BE for short) for
combinatorial optimization applications: we present a parallel version of a
constraint-based local search algorithm that has been implemented on a
multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16
SPUs per blade). This algorithm was chosen because it fits very well the
Cell/BE architecture and requires neither shared memory nor communication
between processors, while retaining a compact memory footprint. We study the
performance on several large optimization benchmarks and show that this
achieves mostly linear time speedups, even sometimes super-linear. This is
possible because the parallel implementation might explore simultaneously
different parts of the search space and therefore converge faster towards the
best sub-space and thus towards a solution. Besides getting speedups, the
resulting times exhibit a much smaller variance, which benefits applications
where a timely reply is critical
TS2PACK: A Two-Level Tabu Search for the Three-dimensional Bin Packing Problem
Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing one
Local Search Techniques for Constrained Portfolio Selection Problems
We consider the problem of selecting a portfolio of assets that provides the
investor a suitable balance of expected return and risk. With respect to the
seminal mean-variance model of Markowitz, we consider additional constraints on
the cardinality of the portfolio and on the quantity of individual shares. Such
constraints better capture the real-world trading system, but make the problem
more difficult to be solved with exact methods. We explore the use of local
search techniques, mainly tabu search, for the portfolio selection problem. We
compare and combine previous work on portfolio selection that makes use of the
local search approach and we propose new algorithms that combine different
neighborhood relations. In addition, we show how the use of randomization and
of a simple form of adaptiveness simplifies the setting of a large number of
critical parameters. Finally, we show how our techniques perform on public
benchmarks.Comment: 22 pages, 3 figure
Satellite downlink scheduling problem: A case study
The synthetic aperture radar (SAR) technology enables satellites to
efficiently acquire high quality images of the Earth surface. This generates
significant communication traffic from the satellite to the ground stations,
and, thus, image downlinking often becomes the bottleneck in the efficiency of
the whole system. In this paper we address the downlink scheduling problem for
Canada's Earth observing SAR satellite, RADARSAT-2. Being an applied problem,
downlink scheduling is characterised with a number of constraints that make it
difficult not only to optimise the schedule but even to produce a feasible
solution. We propose a fast schedule generation procedure that abstracts the
problem specific constraints and provides a simple interface to optimisation
algorithms. By comparing empirically several standard meta-heuristics applied
to the problem, we select the most suitable one and show that it is clearly
superior to the approach currently in use.Comment: 23 page
Portfolio selection using neural networks
In this paper we apply a heuristic method based on artificial neural networks
in order to trace out the efficient frontier associated to the portfolio
selection problem. We consider a generalization of the standard Markowitz
mean-variance model which includes cardinality and bounding constraints. These
constraints ensure the investment in a given number of different assets and
limit the amount of capital to be invested in each asset. We present some
experimental results obtained with the neural network heuristic and we compare
them to those obtained with three previous heuristic methods.Comment: 12 pages; submitted to "Computers & Operations Research
Non-linear great deluge with learning mechanism for solving the course timetabling problem
International audienc
- …