Search results for " optimization."
showing 10 items of 2333 documents
A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics
2006
We propose a new distributed image segmentation algorithm structured as a multiagent system composed of a set of segmentation agents and a coordinator agent. Starting from its own initial image, each segmentation agent performs the iterated conditional modes method, known as ICM, in applications based on Markov random fields, to obtain a sub-optimal segmented image. The coordinator agent diversifies the initial images using the genetic crossover and mutation operators along with the extremal optimization local search. This combination increases the efficiency of our algorithm and ensures its convergence to an optimal segmentation as it is shown through some experimental results.
Strategies for accelerating ant colony optimization algorithms on graphical processing units
2007
Ant colony optimization (ACO) is being used to solve many combinatorial problems. However, existing implementations fail to solve large instances of problems effectively. In this paper we propose two ACO implementations that use graphical processing units to support the needed computation. We also provide experimental results by solving several instances of the well-known orienteering problem to show their features, emphasizing the good properties that make these implementations extremely competitive versus parallel approaches.
Shape optimization of high-energy absorbers
2018
In this paper, a new approach to simulate and to optimize the performances of a crash-box, in terms of energy absorption or acceleration peak, is presented. As soon as the maximum size of the crash-box (longitudinal and transversal dimensions) has been fixed, the new approach allows optimizing the shape of the transversal section and the thickness of the structure. Thanks to the proposed procedure, engineers can easily identify the best crash-box depending on the particular working conditions. The new method has been tested with different cases study by considering different objective functions. The obtained results show the procedure works well and also demonstrate that the optimal number …
A new ESO-based method to find the optimal topology of structures subject to multiple load conditions
2014
In the field of topology optimization problems, the Evolutionary Structural Optimization (ESO) method is one of the most popular and easy to use. When dealing with problems of reasonable difficulty, the ESO method is able to give very good results in reduced times and with a limited request of computational resources. Generally, main applications of this method are addressed to the definition of the optimal topology of a component subjected to a single load condition.In this work, a new methodology, based on the ESO approach, is introduced for the study of the optimal topology of a component subjected to multiple load conditions. The new procedure, entirely developed in the APDL programming…
Fenibuta piemaisījumu kvantitatīvas noteikšanas metodes optimizācija un validācija
2021
Kokina. P., zinātniskie vadītāji: Dr. ķīm. V. Bartkevičs (LU), Dr. ķīm. A. Bolotin (AS Olainfarm). Maģistra darbs, 147 lappuses, 184 attēli, 75 tabulas, 15 literatūras avoti, 16 pielikumi. Latviešu valodā. Maģistra darbā ir veikta divu piemaisījumu(4-amino-3-fenilbutānskābes etilestera hidrohlorīds un 4-fenil-2-pirrolidons) kvantitatīvas noteikšanas analīzes metodes optimizācija un validācija farmaceitiskā preperāta Noofen® aktīvajai farmaceitiskai vielai (AFV) fenibuts. Metodes optimizācija un validācija bija nepieciešama farmaceitiskā preparāta Noofen® kvalitātes kontroles vajadzībām. Fenibuta paraugi tika analizēti ar augsti efektīvo šķidruma hromatogrāfiju (AEŠH). Optimizācija tika vērs…
Distributed and proximity-constrained C-means for discrete coverage control
2018
In this paper we present a novel distributed coverage control framework for a network of mobile agents, in charge of covering a finite set of points of interest (PoI), such as people in danger, geographically dispersed equipment or environmental landmarks. The proposed algorithm is inspired by C-Means, an unsupervised learning algorithm originally proposed for non-exclusive clustering and for identification of cluster centroids from a set of observations. To cope with the agents' limited sensing range and avoid infeasible coverage solutions, traditional C-Means needs to be enhanced with proximity constraints, ensuring that each agent takes into account only neighboring PoIs. The proposed co…
Ockham's Razor in Memetic Computing: Three Stage Optimal Memetic Exploration
2012
Memetic computing is a subject in computer science which considers complex structures as the combination of simple agents, memes, whose evolutionary interactions lead to intelligent structures capable of problem-solving. This paper focuses on memetic computing optimization algorithms and proposes a counter-tendency approach for algorithmic design. Research in the field tends to go in the direction of improving existing algorithms by combining different methods or through the formulation of more complicated structures. Contrary to this trend, we instead focus on simplicity, proposing a structurally simple algorithm with emphasis on processing only one solution at a time. The proposed algorit…
Model identification and local linear convergence of coordinate descent
2020
For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g., support identification for the Lasso) after a finite number of iterations, provided the objective function is regular enough. Results concerning coordinate descent are scarcer and model identification has only been shown for specific estimators, the support-vector machine for instance. In this work, we show that cyclic coordinate descent achieves model identification in finite time for a wide class of functions. In addition, we prove explicit local linear convergence rates for coordinate descent. Extensive experiments on various estimators and on real datasets demonstrate that thes…
Dual Extrapolation for Sparse Generalized Linear Models
2020
International audience; Generalized Linear Models (GLM) form a wide class of regression and classification models, where prediction is a function of a linear combination of the input variables. For statistical inference in high dimension, sparsity inducing regularizations have proven to be useful while offering statistical guarantees. However, solving the resulting optimization problems can be challenging: even for popular iterative algorithms such as coordinate descent, one needs to loop over a large number of variables. To mitigate this, techniques known as screening rules and working sets diminish the size of the optimization problem at hand, either by progressively removing variables, o…
An Empirical Investigation into Deep and Shallow Rule Learning
2021
Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest case, concept learning, this is a disjunctive normal form (DNF) description of the positive class. While it is clear that this is sufficient from a logical point of view because every logical expression can be reduced to an equivalent DNF expression, it could nevertheless be the case that more structured representations, which form deep theories by forming intermediate concept…