Search results for "Heuristic"
showing 10 items of 476 documents
An optimization location scheme for electric charging stations
2013
International audience; Due to environmental issues, electric mobility is one of the mobility alternatives that are receiving a huge attention nowadays. In fact, in the last few years electric vehicles have entered the world's car market. This revolutionary technology requires a fast deployment of electric charging stations since the key issue in this system is recharging the batteries. In this work, we propose an optimized algorithm to locate electric-vehicles charging stations. Different factors and limitations are considered and a real case study is given as an application. We first determine the appropriate strict constraints and cost of charging stations' location; and then we propose …
A Procedure for Selecting Representative Subsamples of a Population from a Simple Random Sample
2015
This paper proposes a procedure for selecting large subsamples drawn from a large simple random sample that are more representative of the population under study. By means of the so-called constant of proportionality, the procedure seeks to maximize the size of the subsample taken from a stratified random sample with proportional allocation, restricting it to a p-value high enough to achieve a good fit using Pearson’s chi-square goodness of fit test. The user has the freedom to choose between a larger subsample with poorer adjustment or a smaller subsample with a better fit. We use the Continuous Sample of Working Lives (CSWL), a set of micro data taken from Spanish Social Security records,…
Backcalculation of airport pavement moduli and thickness using the Lévy Ant Colony Optimization Algorithm
2016
Interpretation of NDTdata is crucial in any Airport Pavement Management System (APMS), in order to implement strategies to maintain airport pavementssince they allow to estimate their remaining life and related maintenance needs and activities. In this paper, the AntColony Optimization algorithmwasused for backcalculation of pavement moduli from surface deflection data. The algorithm’s performances are illustrated and improvement in prediction quality is demonstrated both in terms of goodness of fitness and computational effort. Moreover, it is proved that the proposed algorithm is also able to predict layer thicknesses, taking into account their variation too.
A computational proposal for a robust estimation of the Pareto tail index: An application to emerging markets
2022
Abstract In this work, we backtest and compare, under the VaR risk measure, the fitting performances of three classes of density distributions (Gaussian, Stable and Pareto) with respect to three different types of emerging markets: Egypt, Qatar and Mexico. We also propose a new technique for the estimation of the Pareto tail index by means of the Threshold Accepting (TAVaR) and the Hybrid Particle Swarm Optimization algorithm (H-PSOVaR). Furthermore, we test the accuracy and robustness of our estimates demonstrating the effectiveness of the proposed approach.
A Review of Bias Research in Auditing: Opportunities for Integrating Experimental Psychology and Experimental Economics
2009
The objective of this paper is to show opportunities for integrating psychological and economics research in auditing. For this purpose, auditing research that employs both the methodologies of experimental psychology and experimental economics is collectively reviewed. The review is structured along three fundamental research questions: (1) Are auditors prone to biases; (2) what are the consequences of biased judgment in auditing; and (3) do features of the audit environment interact with the biased judgment? While both the research approach of experimental psychology and experimental economics are employed for addressing these resesarch questions, both approaches differ in their focus and…
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.
Finding optimal finite biological sequences over finite alphabets: the OptiFin toolbox
2017
International audience; In this paper, we present a toolbox for a specific optimization problem that frequently arises in bioinformatics or genomics. In this specific optimisation problem, the state space is a set of words of specified length over a finite alphabet. To each word is associated a score. The overall objective is to find the words which have the lowest possible score. This type of general optimization problem is encountered in e.g 3D conformation optimisation for protein structure prediction, or largest core genes subset discovery based on best supported phylogenetic tree for a set of species. In order to solve this problem, we propose a toolbox that can be easily launched usin…
Learning User's Confidence for Active Learning
2013
In this paper, we study the applicability of active learning in operative scenarios: more particularly, we consider the well-known contradiction between the active learning heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity of the pixel context and user's knowledge of the scene. We propose a filtering scheme based on a classifier that learns the confidence of the user in labeling, thus minimizing the queries where the user would not be able to provide a class for the pixel. The capacity of a model to learn the user's confidence is studied in detail, also showing the effect of resolution is such a …
Active emulation of computer codes with Gaussian processes – Application to remote sensing
2020
Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironically make them less tractable and transparent. This paper introduces an active learning methodology for adaptively constructing surrogate models, i.e. emulators, of such costly computer codes in a multi-output setting. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. It aims to achieve accurate approximations…
A survey of active learning algorithms for supervised remote sensing image classification
2011
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active …