11,028 research outputs found
Automated generation of computationally hard feature models using evolutionary algorithms
This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.A feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.European Commission (FEDER), the Spanish Government and
the Andalusian Government
Key Challenges and Opportunities in Hull Form Design Optimisation for Marine and Offshore Applications
New environmental regulations and volatile fuel
prices have resulted in an ever-increasing need for reduction
in carbon emission and fuel consumption. Designs of marine
and offshore vessels are more demanding with complex
operating requirements and oil and gas exploration
venturing into deeper waters and hasher environments.
Combinations of these factors have led to the need to
optimise the design of the hull for the marine and offshore
industry. The contribution of this paper is threefold. Firstly,
the paper provides a comprehensive review of the state-ofthe-
art techniques in hull form design. Specifically, it
analyses geometry modelling, shape transformation,
optimisation and performance evaluation. Strengths and
weaknesses of existing solutions are also discussed.
Secondly, key challenges of hull form optimisation specific
to the design of marine and offshore vessels are identified
and analysed. Thirdly, future trends in performing hull
form design optimisation are investigated and possible
solutions proposed. A case study on the design optimisation
of bulbous bow for passenger ferry vessel to reduce wavemaking
resistance is presented using NAPA software.
Lastly, main issues and challenges are discussed to stimulate
further ideas on future developments in this area, including
the use of parallel computing and machine intelligence
Surrogate Assisted Optimisation for Travelling Thief Problems
The travelling thief problem (TTP) is a multi-component optimisation problem
involving two interdependent NP-hard components: the travelling salesman
problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP
solvers modify the underlying TSP and KP solutions in an iterative and
interleaved fashion. The TSP solution (cyclic tour) is typically changed in a
deterministic way, while changes to the KP solution typically involve a random
search, effectively resulting in a quasi-meandering exploration of the TTP
solution space. Once a plateau is reached, the iterative search of the TTP
solution space is restarted by using a new initial TSP tour. We propose to make
the search more efficient through an adaptive surrogate model (based on a
customised form of Support Vector Regression) that learns the characteristics
of initial TSP tours that lead to good TTP solutions. The model is used to
filter out non-promising initial TSP tours, in effect reducing the amount of
time spent to find a good TTP solution. Experiments on a broad range of
benchmark TTP instances indicate that the proposed approach filters out a
considerable number of non-promising initial tours, at the cost of omitting
only a small number of the best TTP solutions
Performance Analysis of Optimization Methods in PSE Applications. Mathematical Programming Versus Grid-based Multi-parametric Genetic Algorithms
Due to their large variety of applications in the PSE area, complex optimisation problems are of high interest for the scientific community. As a consequence, a great effort is made for developing efficient solution techniques. The choice of the relevant technique for the treatment of a given problem has already been studied for batch plant design issues. However,most works reported in the dedicated literature classically considered item sizes as continuous variables. In a view of realism, a similar approach is proposed in this paper, with discrete variables representing equipment capacities. The numerical results enable to evaluate the performances of two mathematical programming (MP) solvers embedded within the GAMS package and a genetic algorithm (GA), on a set of seven increasing complexity examples. The necessarily huge number of runs for the GA could be performed within a computational framework basedon a grid infrastructure; however, since the MP methods were tackled through single-computer computations, the CPU time comparison are reported for this one-PC working mode. On the one hand, the high combinatorial effect induced by the new discrete variables heavily penalizes the GAMS modules, DICOPTĂŸĂŸand SBB. On the other hand, the Genetic Algorithm proves its superiority, providing quality solutions within acceptable computational times, whatever the considered example
SCOR: Software-defined Constrained Optimal Routing Platform for SDN
A Software-defined Constrained Optimal Routing (SCOR) platform is introduced
as a Northbound interface in SDN architecture. It is based on constraint
programming techniques and is implemented in MiniZinc modelling language. Using
constraint programming techniques in this Northbound interface has created an
efficient tool for implementing complex Quality of Service routing applications
in a few lines of code. The code includes only the problem statement and the
solution is found by a general solver program. A routing framework is
introduced based on SDN's architecture model which uses SCOR as its Northbound
interface and an upper layer of applications implemented in SCOR. Performance
of a few implemented routing applications are evaluated in different network
topologies, network sizes and various number of concurrent flows.Comment: 19 pages, 11 figures, 11 algorithms, 3 table
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
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation
Evolutionary algorithms have been frequently applied to constrained
continuous optimisation problems. We carry out feature based comparisons of
different types of evolutionary algorithms such as evolution strategies,
differential evolution and particle swarm optimisation for constrained
continuous optimisation. In our study, we examine how sets of constraints
influence the difficulty of obtaining close to optimal solutions. Using a
multi-objective approach, we evolve constrained continuous problems having a
set of linear and/or quadratic constraints where the different evolutionary
approaches show a significant difference in performance. Afterwards, we discuss
the features of the constraints that exhibit a difference in performance of the
different evolutionary approaches under consideration.Comment: 16 Pagesm 2 Figure
Integrating continuous differential evolution with discrete local search for meander line RFID antenna design
The automated design of meander line RFID antennas is a discrete self-avoiding walk(SAW) problem for which efficiency is to be maximized while resonant frequency is to beminimized. This work presents a novel exploration of how discrete local search may beincorporated into a continuous solver such as differential evolution (DE). A prior DE algorithmfor this problem that incorporates an adaptive solution encoding and a bias favoringantennas with low resonant frequency is extended by the addition of the backbite localsearch operator and a variety of schemes for reintroducing modified designs into the DEpopulation. The algorithm is extremely competitive with an existing ACO approach and thetechnique is transferable to other SAW problems and other continuous solvers. The findingsindicate that careful reintegration of discrete local search results into the continuous populationis necessary for effective performance
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