Search results for " optimization."
showing 10 items of 2333 documents
DEVELOPMENT AND APPLICATIONS OF A VIRTUAL HYBRID PLATFORM FOR MULTISCALE ANALYSIS OF ADVANCED STRUCTURES OF AIRCRAFT (DEVISU)
2022
This paper outlines the main findings of the project “DEvelopment and applications of a VIrtual hybrid platform for multiscale analysis of advanced StructUres of aircraft” (DEVISU), which deals with failure of composite structures and noise/vibration reduction, along with investigation of new materials for aerospace applications.
Adaptive BEM for Low Noise Propeller Design
2009
A potential-based Boundary Element Method is presented for the aerodynamic and acoustic design of propel- lers at on- and off-design point conditions. Using an adaptive method, a family of airfoil sections is selected to produce the required performance (thrust, torque and efficiency versus advance ratio) at different cruise flight levels. Climb condi- tions are also considered in order to check the off-design point performance. Once the available airfoil data have been stored in a database, the code processes the families of airfoils to generate a complete geometry for a propeller of the specified performance with an optimized noise emission. The computational scheme adjusts the blade geom…
Geometric interpretation of the optimality conditions in multifacility location and applications
1991
Geometrical optimality conditions are developed for the minisum multifacility location problem involving any norm. These conditions are then used to derive sufficient conditions for coincidence of facilities at optimality; an example is given to show that these coincidence conditions seem difficult to generalize.
Closedness properties in team learning of recursive functions
1997
This paper investigates closedness properties in relation with team learning of total recursive functions. One of the first problems solved for any new identification types is the following: “Does the identifiability of classes U1 and U2 imply the identifiability of U1∪U2?” In this paper we are interested in a more general question: “Does the identifiability of every union of n−1 classes out of U1,...,Un imply the identifiability of U1∪...∪Un?” If the answer is positive, we call such identification type n-closed. We show that n-closedness can be equivalently formulated in terms of team learning. After that we find for which n team identification in the limit and team finite identification t…
Introduction to General Duality Theory for Multi-Objective Optimization
1992
This is intended as a comprehensive introduction to the duality theory for vector optimization recently developed by C. Malivert and the present author [3]. It refers to arbitrarily given classes of mappings (dual elements) and extends the general duality theory proposed for scalar optimization by E. Balder, S. Kurcyusz and the present author [1] and P. Lindberg.
Ensuring High Performance of Consensus-Based Estimation by Lifetime Maximization in WSNs
2015
The estimation of a parameter corrupted by noise is a common tasks in wireless sensor networks, where the deployed nodes cooperate in order to improve their own inaccurate observations. This cooperation usually involves successive data exchanges and local information updates until a global consensus value is reached. The quality of the final estimator depends on the amount of collected observations, hence the number of active nodes. Moreover, the inherent iterative nature of the consensus process involves a certain energy consumption. Since the devices composing the network are usually battery powered, nodes becoming inactive due to battery depletion emerges as a serious problem. In this wo…
GRASP with exterior path-relinking and restricted local search for the multidimensional two-way number partitioning problem
2017
In this work, we tackle multidimensional two-way number partitioning (MDTWNP) problem by combining GRASP with Exterior Path Relinking. In the last few years, the combination of GRASP with path relinking (PR) has emerged as a highly effective tool for finding high-quality solutions for several difficult problems in reasonable computational time. However, in most of the cases, this hybridisation is limited to the variant known as interior PR. Here, we couple GRASP with the "exterior form" of path relinking and perform extensive experimentation to evaluate this variant. In addition, we enhance our GRASP with PR method with a novel local search method specially designed for the MDTWNP problem. …
Multi-sensor Fusion through Adaptive Bayesian Networks
2011
Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-off between performance and cost, by adapting the operating mode of the underlying sensory devices. Moreover, self-configuration of the nodes providing the evidence to the Bayesian network is carried out by means of an on-line multi-objective optimization.
An environment based approach for the ant colony convergence
2020
Abstract Ant colony optimization (ACO) algorithms are a bio inspired solutions which have been very successful in combinatorial problem solving, also known as NP-hard problems, including transportation system optimization. As opposed to exact methods, which could give the best results of a tested problem, this meta-heuristics is based on the stochastic logic but not on theoretical mathematics demonstration (or only on certain well defined applications). According to this, the weak point of this meta-heuristics is his convergence, its termination condition. We can finds many different termination criteria in the scientific literature, yet most of them are costly in resources and unsuitable f…
Towards a multilevel ant colony optimization
2014
Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014 Ant colony optimization is a metaheuristic approach for solving combinatorial optimization problems which belongs to swarm intelligence techniques. Ant colony optimization algorithms are one of the most successful strands of swarm intelligence which has already shown very good performance in many combinatorial problems and for some real applications. This thesis introduces a new multilevel approach for ant colony optimization to solve the NP-hard problems shortest path and traveling salesman. We have reviewed different elements of multilevel algorithm which helped us in construction of our proposed mu…