982 research outputs found
State-of-the-art in aerodynamic shape optimisation methods
Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners
Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models
Multivariate count data are defined as the number of items of different
categories issued from sampling within a population, which individuals are
grouped into categories. The analysis of multivariate count data is a recurrent
and crucial issue in numerous modelling problems, particularly in the fields of
biology and ecology (where the data can represent, for example, children counts
associated with multitype branching processes), sociology and econometrics. We
focus on I) Identifying categories that appear simultaneously, or on the
contrary that are mutually exclusive. This is achieved by identifying
conditional independence relationships between the variables; II)Building
parsimonious parametric models consistent with these relationships; III)
Characterising and testing the effects of covariates on the joint distribution
of the counts. To achieve these goals, we propose an approach based on
graphical probabilistic models, and more specifically partially directed
acyclic graphs
Motion Planning for Autonomous Ground Vehicles Using Artificial Potential Fields: A Review
Autonomous ground vehicle systems have found extensive potential and
practical applications in the modern world. The development of an autonomous
ground vehicle poses a significant challenge, particularly in identifying the
best path plan, based on defined performance metrics such as safety margin,
shortest time, and energy consumption. Various techniques for motion planning
have been proposed by researchers, one of which is the use of artificial
potential fields. Several authors in the past two decades have proposed various
modified versions of the artificial potential field algorithms. The variations
of the traditional APF approach have given an answer to prior shortcomings.
This gives potential rise to a strategic survey on the improved versions of
this algorithm. This study presents a review of motion planning for autonomous
ground vehicles using artificial potential fields. Each article is evaluated
based on criteria that involve the environment type, which may be either static
or dynamic, the evaluation scenario, which may be real-time or simulated, and
the method used for improving the search performance of the algorithm. All the
customized designs of planning models are analyzed and evaluated. At the end,
the results of the review are discussed, and future works are proposed
An adaptive hybrid genetic-annealing approach for solving the map problem on belief networks
Genetic algorithms (GAs) and simulated annealing (SA) are two important search methods that have been used successfully in solving difficult problems such as combinatorial optimization problems. Genetic algorithms are capable of wide exploration of the search space, while simulated annealing is capable of fine tuning a good solution. Combining both techniques may result in achieving the benefits of both and improving the quality of the solutions obtained. Several attempts have been made to hybridize GAs and SA. One such attempt was to augment a standard GA with simulated annealing as a genetic operator. SA in that case acted as a directed or intelligent mutation operator as opposed to the random, undirected mutation operator of GAs. Although using this technique showed some advantages over GA used alone, one problem was to find fixed global annealing parameters that work for all solutions and all stages in the search process. Failing to find optimum annealing parameters affects the quality of the solution obtained and may degrade performance. In this research, we try to overcome this weakness by introducing an adaptive hybrid GA - SA algorithm, in which simulated annealing acts as a special case of mutation. However, the annealing operator used in this technique is adaptive in the sense that the annealing parameters are evolved and optimized according to the requirements of the search process. Adaptation is expected to help guide the search towards optimum solutions with minimum effort of parameter optimization. The algorithm is tested in solving an important NP-hard problem, which is the MAP (Maximum a-Posteriori) assignment problem on BBNs (Bayesian Belief Networks). The algorithm is also augmented with some problem specific information used to design a new GA crossover operator. The results obtained from testing the algorithm on several BBN graphs with large numbers of nodes and different network structures indicate that the adaptive hybrid algorithm provides an improvement of solution quality over that obtained by GA used alone and GA augmented with standard non-adaptive simulated annealing. Its effect, however, is more profound for problems with large numbers of nodes, which are difficult for GA alone to solve
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