27,028 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
Quasiconvex Programming
We define quasiconvex programming, a form of generalized linear programming
in which one seeks the point minimizing the pointwise maximum of a collection
of quasiconvex functions. We survey algorithms for solving quasiconvex programs
either numerically or via generalizations of the dual simplex method from
linear programming, and describe varied applications of this geometric
optimization technique in meshing, scientific computation, information
visualization, automated algorithm analysis, and robust statistics.Comment: 33 pages, 14 figure
Development of an automated aircraft subsystem architecture generation and analysis tool
Purpose – The purpose of this paper is to present a new computational framework to address future
preliminary design needs for aircraft subsystems. The ability to investigate multiple candidate
technologies forming subsystem architectures is enabled with the provision of automated architecture
generation, analysis and optimization. Main focus lies with a demonstration of the frameworks
workings, as well as the optimizers performance with a typical form of application problem.
Design/methodology/approach – The core aspects involve a functional decomposition, coupled
with a synergistic mission performance analysis on the aircraft, architecture and component levels.
This may be followed by a complete enumeration of architectures, combined with a user defined
technology filtering and concept ranking procedure. In addition, a hybrid heuristic optimizer, based on
ant systems optimization and a genetic algorithm, is employed to produce optimal architectures in both
component composition and design parameters. The optimizer is tested on a generic architecture
design problem combined with modified Griewank and parabolic functions for the continuous space.
Findings – Insights from the generalized application problem show consistent rediscovery of the
optimal architectures with the optimizer, as compared to a full problem enumeration. In addition
multi-objective optimization reveals a Pareto front with differences in component composition as well
as continuous parameters.
Research limitations/implications – This paper demonstrates the frameworks application on a
generalized test problem only. Further publication will consider real engineering design problems.
Originality/value – The paper addresses the need for future conceptual design methods of complex
systems to consider a mixed concept space of both discrete and continuous nature via automated methods
Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms
The authors propose the implementation of hybrid Fuzzy Logic-Genetic
Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly
sequence of products. The GA-Fuzzy Logic approach is implemented onto two
levels. The first level of hybridization consists of the development of a Fuzzy
controller for the parameters of an assembly or disassembly planner based on
GAs. This controller acts on mutation probability and crossover rate in order
to adapt their values dynamically while the algorithm runs. The second level
consists of the identification of theoptimal assembly or disassembly sequence
by a Fuzzy function, in order to obtain a closer control of the technological
knowledge of the assembly/disassembly process. Two case studies were analyzed
in order to test the efficiency of the Fuzzy-GA methodologies
BARD: Better Automated Redistricting
BARD is the first (and at time of writing, only) open source software package for general redistricting and redistricting analysis. BARD provides methods to create, display, compare, edit, automatically refine, evaluate, and profile political districting plans. BARD aims to provide a framework for scientific analysis of redistricting plans and to facilitate wider public participation in the creation of new plans. BARD facilitates map creation and refinement through command-line, graphical user interface, and automatic methods. Since redistricting is a computationally complex partitioning problem not amenable to an exact optimization solution, BARD implements a variety of selectable metaheuristics that can be used to refine existing or randomly-generated redistricting plans based on user-determined criteria. Furthermore, BARD supports automated generation of redistricting plans and profiling of plans by assigning different weights to various criteria, such as district compactness or equality of population. This functionality permits exploration of trade-offs among criteria. The intent of a redistricting authority may be explored by examining these trade-offs and inferring which reasonably observable plans were not adopted. Redistricting is a computationally-intensive problem for even modest-sized states. Performance is thus an important consideration in BARD's design and implementation. The program implements performance enhancements such as evaluation caching, explicit memory management, and distributed computing across snow clusters.
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