1,126 research outputs found
An Efficient Implementation of the Robust Tabu Search Heuristic for Sparse Quadratic Assignment Problems
We propose and develop an efficient implementation of the robust tabu search
heuristic for sparse quadratic assignment problems. The traditional
implementation of the heuristic applicable to all quadratic assignment problems
is of O(N^2) complexity per iteration for problems of size N. Using multiple
priority queues to determine the next best move instead of scanning all
possible moves, and using adjacency lists to minimize the operations needed to
determine the cost of moves, we reduce the asymptotic complexity per iteration
to O(N log N ). For practical sized problems, the complexity is O(N)
Hybrid Algorithm for Solving the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is a combinatorial optimization problem; it belongs to the class of NP-hard problems. This problem is applied in various fields such as hospital layout, scheduling parallel production lines and analyzing chemical reactions for organic compounds. In this paper we propose an application of Golden Ball algorithm mixed with Simulated Annealing (GBSA) to solve QAP. This algorithm is based on different concepts of football. The simulated annealing search can be blocked in a local optimum due to the unacceptable movements; our proposed strategy guides the simulated annealing search to escape from the local optima and to explore in an efficient way the search space. To validate the proposed approach, numerous simulations were conducted on 64 instances of QAPLIB to compare GBSA with existing algorithms in the literature of QAP. The obtained numerical results show that the GBSA produces optimal solutions in reasonable time; it has the better computational time. This work demonstrates that our proposed adaptation is effective in solving the quadratic assignment problem
Hybridization as Cooperative Parallelism for the Quadratic Assignment Problem
International audienceThe Quadratic Assignment Problem is at the core of several real-life applications. Finding an optimal assignment is computationally very difficult, for many useful instances. The best results are obtained with hybrid heuristics, which result in complex solvers. We propose an alternate solution where hybridization is obtain by means of parallelism and cooperation between simple single-heuristic solvers. We present experimental evidence that this approach is very efficient and can effectively solve a wide variety of hard problems, often surpassing state-of-the-art systems
GPU-accelerated Parallel Solutions to the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is an important combinatorial
optimization problem with applications in many areas including logistics and
manufacturing. QAP is known to be NP-hard, a computationally challenging
problem, which requires the use of sophisticated heuristics in finding
acceptable solutions for most real-world data sets.
In this paper, we present GPU-accelerated implementations of a 2opt and a
tabu search algorithm for solving the QAP. For both algorithms, we extract
parallelism at multiple levels and implement novel code optimization techniques
that fully utilize the GPU hardware. On a series of experiments on the
well-known QAPLIB data sets, our solutions, on average run an
order-of-magnitude faster than previous implementations and deliver up to a
factor of 63 speedup on specific instances. The quality of the solutions
produced by our implementations of 2opt and tabu is within 1.03% and 0.15% of
the best known values. The experimental results also provide key insight into
the performance characteristics of accelerated QAP solvers. In particular, the
results reveal that both algorithmic choice and the shape of the input data
sets are key factors in finding efficient implementations.Comment: 25 pages, 9 figures; parts of this work appeared as short papers in
XSEDE14 and XSEDE15 conferences. This version of the paper is a substantial
extension of previous work with optimizations for newer GPU platforms and
extended experimental result
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
A metaheuristic multi-criteria optimisation approach to portfolio selection
Portfolio selection is concerned with selecting from of a universe of assets the ones in which one wishes to invest and the amount of the investment. Several criteria can be used for portfolio selection, and the resulting approaches can be classified as being either active or passive. The two approaches are thought to be mutually exclusive, but some authors have suggested combining them in a unified framework. In this work, we define a multi-criteria optimisation problem in which the two types of approaches are combined, and we introduce a hybrid metaheuristic that combines local search and quadratic programming to obtain an approximation of the Pareto set. We experimentally analyse this approach on benchmarks from two different instance classes: these classes refer to the same indexes, but they use two different return representations. Results show that this metaheuristic can be effectively used to solve multi-criteria portfolio selection problems. Furthermore, with an experiment on a set of instances coming from a different financial scenario, we show that the results obtained by our metaheuristic are robust with respect to the return representation used
Reinforcement Learning: A Survey
This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file
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