The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Dynamic multi-objective optimisation (DMOO) entails solving optimisation problems with more than one objective, where at least one objective changes over time. Normally at least two of the objectives are in conflict with one another. Therefore, a single solution does not exist and the goal of an algorithm is to find for each environment a set of solutions that are both diverse and as close as possible...
This paper investigates various strategies for the management of solution space diversity within the context of a meta-hyper heuristic algorithm. The adaptive local search meta-hyper heuristic (ALSHH), which adaptively applies a local search algorithm when the population diversity strays outside a predetermined solution space diversity profile, is proposed. ALSHH was shown to compare favourably with...
A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms...
This article presents the experience in the development of a semantic platform for decision support based on the DOODA cycle that aims to help dengue epidemics control. We discuss the formal representation of data as type of dengue, hydration tents and relief efforts classified in epidemiology, infrastructure and civil defense domains on an ontology that associated with a reasoner and a map engine,...
This work deals with satellite sun sensor placement using genetic algorithms. For a simplified but realistic problem scenario, this problem is solved and many simulation results are shown. The proposed methodology, which relies on numerical shadow analysis and multi-objective optimization, is discussed and a typical satellite design problem, that is frequently solved by a try-and-error approach, is...
This work investigates the effect of different data structures on the performance and accuracy of VG-RAM-based classifiers. This weightless neural model is based on RAM nodes having very large address input, what suggests the use of special data structures in order to deal with space and time computational costs. Four different data structures are explored, including the classical one used in recent...
In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations using Local Linear Regression (LLR). An experimental evaluation is conducted to show the performance of the SDPP and LLR and compare it with a number...
Simulation of home use of electric energy is a very powerful tool for the purpose of studying, planning and managing at electric energy distribution companies. This paper presents a NetLogo-based multi-agent system for energy consumption simulation in residential areas. Several possible consumers profiles and household appliances with different powers are modeled and simulated using computational...
In this paper, we propose a hybrid Multiobjective Estimation of Distribution Algorithm based on Artificial Bee Colonies and Clusters (MOEDABC) to solve multiobjective optimization problems with continuous variables. This algorithm is inspired in the organization and division of work in a bee colony and employs techniques from estimation of distribution algorithms. To improve some estimations we also...
A RSSI-based localization system on a home wireless sensor network is proposed in this work. In order to support a robot assistant in pick-and-place tasks, our current system is capable of estimating the localization of an object using the signal strength received by a mobile device in a ZigBee sensor network. Two models were utilized (a) log-distance path loss - model in which signal lost has a random...
Context-Aware Recommender Systems (CARS) have emerged as a different way of providing more precise and interesting recommendations through the use of data about the context in which consumers buy goods and/or services. CARS consider not only the ratings given to items by consumers (users), but also the context attributes related to these ratings. Several algorithms and methods have been proposed in...
This work presents a methodology for pattern classification from multispectral images acquired by the HSS airborne sensor. In order to achieve this purpose, a conjunction of Artificial Neural Network and Principal Components Analysis has been used. The results indicate that this approach can be alternatively employed in multispectral images to separate materials with specific characteristics based...
Acute leukemia classification into its Myeloid and Lymphoblastic subtypes is usually accomplished according to the morphological appearance of the tumor. Nevertheless, cells from the two subtypes can have similar histopathological appearance, which makes screening procedures very difficult. Correct classification of patients in the initial phases of the disease would allow doctors to properly prescribe...
In this paper we address the issues of how incrementally chunking learned action rules of increasing length and complexity can assist in solving problems of ever greater complexity. To this end, we employ a micro-world with simple objects and simplified physical behaviors. The agent first learns some basic elemental rules capturing the fundamental physical behaviors of the agent itself, the objects...
Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data,...
This paper makes a study about optimal supply of the energy service, using simulations of network operation scenarios, in order to optimize resources and minimize the variables: operation cost, energy losses, generation cost and consumers shedding. These simulations create optimal operation models of the network, allowing the system operator obtain knowledge to take pre-established procedures that...
Heterogeneous particle swarm optimizers (HPSO) add multiple search behaviors to the swarm. This is done by allowing particles to utilize different update equations to each other. Dynamic and adaptive HPSO algorithms allow the particles to change their behaviors during the search. A number of factors come into play when dealing with the different behaviors, one of which is deciding when a particle...
Bin packing is a classical combinatorial optimization problem which has a wide range of real-world applications in industry, logistics, transport, parallel computing, circuit design and other domains. While usually presented as discrete problems, we consider here continuous packing problems including curve shapes, and model these problems as continuous optimization problems with a multi-objective...
Differently from traditional machine learning techniques applied to data classification, high level classification considers not only the physical features of the data (distance, similarity or distribution), but also the pattern formation of the data. In this latter case, a set of complex network measures are employed because of their abilities to capture spatial, functional and topological relations...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.