27,172 research outputs found
A Tabu Search Based Approach for Graph Layout
This paper describes an automated tabu search based method for drawing general graph layouts with straight lines. To our knowledge, this is the first time tabu methods have been applied to graph drawing. We formulated the task as a multi-criteria optimization problem with a number of
metrics which are used in a weighted fitness function to measure the aesthetic
quality of the graph layout. The main goal of this work is to speed up the graph
layout process without sacrificing layout quality. To achieve this, we use a tabu
search based method that goes through a predefined number of iterations to minimize
the value of the fitness function. Tabu search always chooses the best solution in
the neighbourhood. This may lead to cycling, so a tabu list is used to store moves
that are not permitted, meaning that the algorithm does not choose previous
solutions for a set period of time. We evaluate the method according to the time
spent to draw a graph and the quality of the drawn graphs. We give experimental
results applied on random graphs and we provide statistical evidence that our
method outperforms a fast search-based drawing method (hill climbing) in execution
time while it produces comparably good graph layouts.We also demonstrate the method
on real world graph datasets to show that we can reproduce similar results in a
real world setting
Representing Space: A Hybrid Genetic Algorithm for Aesthetic Graph Layout
This paper describes a hybrid Genetic Algorithm (GA) that is used to improve the layout of a graph according to a number of aesthetic criteria. The GA incorporates spatial and topological information by operating directly with a graph based representation. Initial results show this to be a promising technique for positioning graph nodes on a surface and may form the basis of a more general approach for problems involving multi-criteria spatial optimisation
An agent-based evolutionary approach for manufacturing system layout design
Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de
Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de ComputadoresIn this thesis it is presented an approach to the problem of layout design for a manufacturing system, which is an important part of its design stage, given that it has influence in the system efficiency and,
therefore, in its output rate and fault handling capabilities.
The presented approach is based on a Genetic Algorithm (GA) that, by using information provided by the the user through an ontology file, and by using algorithms from graph-theory, designs the layout of a manufacturing system. The instances of the ontology represent manufacturing resources and their characteristics that, when they are being used by the algorithm, are encoded in chromosomes and in their genes.
The algorithm begins with a number of chromosomes with low fitness which, with the directed evolution provided by the algorithm, that is restricted by the control parameters that might be tunned by the user, improve with the passing of the new generations. It is considered that the fittest solution is the one that connects, in order, all the resources required by the manufacturing plan, described in the
ontology, without the occurrence of overlaps when the layout is constructed.
The configuration presented by the transport system that handles parts and materials, in the selected layout, is only dependent on the available resources and on the fitness function used by the GA, being that the last cannot be changed by the user. This approach differs from others by positioning simultaneously all the components of the manufacturing system and not only workstations or transport system.
The solution is directed to evolvable assembly systems, purpose for which it was implemented inside an agent, so it can be integrated in a Multiagent System (MAS) to be used in the control of a manufacturing system with minimal changes.
Keywords: layout design, manufacturing system, multiagent system, ontology, genetic algorithm
Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids
Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively)
Improving Search-Based Schematic Layout by Parameter Manipulation
This paper reports on a method to improve the automated layout of schematic diagrams
by widening the search space examined by the system. In search-based layout methods
there are typically a number of parameters that control the search algorithm which do
not affect the fitness function, but nevertheless have an impact on the final layout. We
explore how varying three parameters (grid spacing, the starting distance of allowed
node movement and the number of iterations) affects the resultant diagram in a hill-
climbing layout system. Using an iterative process, we produce diagram layouts that are
significantly better than those produced by ad-hoc parameter settings
Exploring Local Optima in Schematic Layout
In search-based graph drawing methods there are
typically a number of parameters that control the search algorithm.
These parameters do not affect the ?tness function, but
nevertheless have an impact on the ?nal layout. One such search
method is hill climbing, and, in the context of schematic layout, we
explore how varying three parameters (grid spacing, the starting
distance of allowed node movement and the number of iterations)
affects the resultant diagram. Although we cannot characterize
schematics completely and so cannot yet automatically assign
parameters for diagrams, we observe that when parameters are
set to values that increase the search space, they also tend to
improve the ?nal layout. We come to the conclusion that hillclimbing
methods for schematic layout are more prone to reaching
local optima than had previously been expected and that a wider
search, as described in this paper, can mitigate this, so resulting
in a better layout
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