11,975 research outputs found
A HYBRID HOPFIELD NEURAL NETWORK AND TABU SEARCH ALGORITHM TO SOLVE ROUTING PROBLEM IN COMMUNICATION NETWORK
The development of hybrid algorithms for solving complex optimization problems focuses on enhancing the strengths and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of these approaches to find superior solutions to solve optimization problems. Optimal routing in communication network is considering a complex optimization problem. In this paper we propose a hybrid Hopfield Neural Network (HNN) and Tabu Search (TS) algorithm, this algorithm called hybrid HNN-TS algorithm. The paradigm of this hybridization is embedded. We embed the short-term memory and tabu restriction features from TS algorithm in the HNN model. The short-term memory and tabu restriction control the neuron selection process in the HNN model in order to get around the local minima problem and find an optimal solution using the HNN model to solve complex optimization problem. The proposed algorithm is intended to find the optimal path for packet transmission in the network which is fills in the field of routing problem. The optimal path that will be selected is depending on 4-tuples (delay, cost, reliability and capacity). Test results show that the propose algorithm can find path with optimal cost and a reasonable number of iterations. It also shows that the complexity of the network model wonât be a problem since the neuron selection is done heuristically
A Two-Stage Approach for Routing Multiple Unmanned Aerial Vehicles with Stochastic Fuel Consumption
The past decade has seen a substantial increase in the use of small unmanned
aerial vehicles (UAVs) in both civil and military applications. This article
addresses an important aspect of refueling in the context of routing multiple
small UAVs to complete a surveillance or data collection mission. Specifically,
this article formulates a multiple-UAV routing problem with the refueling
constraint of minimizing the overall fuel consumption for all of the vehicles
as a two-stage stochastic optimization problem with uncertainty associated with
the fuel consumption of each vehicle. The two-stage model allows for the
application of sample average approximation (SAA). Although the SAA solution
asymptotically converges to the optimal solution for the two-stage model, the
SAA run time can be prohibitive for medium- and large-scale test instances.
Hence, we develop a tabu-search-based heuristic that exploits the model
structure while considering the uncertainty in fuel consumption. Extensive
computational experiments corroborate the benefits of the two-stage model
compared to a deterministic model and the effectiveness of the heuristic for
obtaining high-quality solutions.Comment: 18 page
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
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)
An oil pipeline design problem
Copyright @ 2003 INFORMSWe consider a given set of offshore platforms and onshore wells producing known (or estimated) amounts of oil to be connected to a port. Connections may take place directly between platforms, well sites, and the port, or may go through connection points at given locations. The configuration of the network and sizes of pipes used must be chosen to minimize construction costs. This problem is expressed as a mixed-integer program, and solved both heuristically by Tabu Search and Variable Neighborhood Search methods and exactly by a branch-and-bound method. Two new types of valid inequalities are introduced. Tests are made with data from the South Gabon oil field and randomly generated problems.The work of the first author was supported by NSERC grant #OGP205041. The work of the second author was supported by FCAR (Fonds pour la Formation des Chercheurs et lâAide Ă la Recherche) grant #95-ER-1048, and NSERC grant #GP0105574
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