Search results for "methodologies"
showing 10 items of 2106 documents
Some Aspects Regarding the Application of the Ant Colony Meta-heuristic to Scheduling Problems
2010
Scheduling is one of the most complex problems that appear in various fields of activity, from industry to scientific research, and have a special place among the optimization problems In our paper we present the results of our computational study i.e an Ant Colony Optimization algorithm for the Resource-Constrained Project Scheduling Problem that uses dynamic pheromone evaporation.
Experiments on a Prey Predators System
2003
The paper describes a prey-predators system devoted to perform experiments on concurrent complex environment. The problem has be treated as an optimization problem. The prey goal is to escape from the predators reaching its lair, while predators want to capture the prey. At the end of the 19th century, Pareto found an optimal solutions for decision problems regarding more than one criterion at the same time. In most cases this ‘Pareto-set’ cannot be determined analytically or the computation time could be exponential. In such cases, evolutionary Algorithms (EA) are powerful optimization tools capable of finding optimal solutions of multi-modal problems. Here, both prey and predators learn i…
A Projected Algebraic Multigrid Method for Linear Complementarity Problems
2011
We present an algebraic version of an iterative multigrid method for obstacle problems, called projected algebraic multigrid (PAMG) here. We show that classical AMG algorithms can easily be extended to deal with this kind of problem. This paves the way for efficient multigrid solution of obstacle problems with partial differential equations arising, for example, in financial engineering.
A genetic algorithm for discrete tomography reconstruction
2007
The aim of this paper is the description of an experiment carried out to verify the robustness of two different approaches for the reconstruction of convex polyominoes in discrete tomography. This is a new field of research, because it differs from classic computerized tomography, and several problems are still open. In particular, the stability problem is tackled by using both a modified version of a known algorithm and a new genetic approach. The effect of both, instrumental and quantization noises has been considered too. © 2007 Springer Science+Business Media, LLC.
Decision Making on Pareto Front Approximations with Inherent Nondominance
2011
t Approximating the Pareto fronts of nonlinear multiobjective optimization problems is considered and a property called inherent nondominance is proposed for such approximations. It is shown that an approximation having the above property can be explored by interactively solving a multiobjective optimization problem related to it. This exploration can be performed with available interactive multiobjective optimization methods. The ideas presented are especially useful in solving computationally expensive multiobjective optimization problems with costly function value evaluations. peerReviewed
Using Fourier local magnitude in adaptive smoothness constraints in motion estimation
2007
Like many problems in image analysis, motion estimation is an ill-posed one, since the available data do not always sufficiently constrain the solution. It is therefore necessary to regularize the solution by imposing a smoothness constraint. One of the main difficulties while estimating motion is to preserve the discontinuities of the motion field. In this paper, we address this problem by integrating the motion magnitude information obtained by the Fourier analysis into the smoothness constraint, resulting in an adaptive smoothness. We describe how to achieve this with two different motion estimation approaches: the Horn and Schunck method and the Markov Random Field (MRF) modeling. The t…
Finding Satisfactory Near-Optimal Solutions in Location Problems
2003
We develope and analyze a heuristic procedure to solve a fuzzy version of the p-median problem in which we allow part of the demand not to be covered in order to reduce the transport cost. This can be used to improve a given solution of the crisp p-median problem as well as to give to the decision-maker a range of alternative locations that can be adequate according to his or her own criteria.
Multiobjective ant colony search algorithm optimal electrical distribution system planning
2005
A dynamic multiobjective, MO, algorithm based on the ant colony search, the multiobjective ant colony search algorithm, MOACS, is presented. The application domain is that of dynamic planning for electrical distribution systems. A time horizon of H years has been considered during which the distribution system are modified according to the new internal (loads) and external (market, reliability, power quality) requirements. In this scenario, the objectives the Authors consider most important for utilities in strategical planning are: the quality requirement connected to the decrease of the expected number of interruptions per year and customer, in the considered time frame, and the choice fo…
Large multiple neighborhood search for the clustered vehicle-routing problem
2018
Abstract The clustered vehicle-routing problem is a variant of the classical capacitated vehicle-routing problem in which customers are partitioned into clusters, and it is assumed that each cluster must have been served completely before the next cluster is served. This decomposes the problem into three subproblems, i.e., the assignment of clusters to routes, the routing inside each cluster, and the sequencing of the clusters in the routes. The second task requires the solution of several Hamiltonian path problems, one for each possibility to route through the cluster. We pre-compute the Hamiltonian paths for every pair of customers of each cluster. We present a large multiple neighborhood…
A Memetic Algorithm for Binary Image Reconstruction
2008
This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.