314,991 research outputs found
Global geometry optimization of clusters using a growth strategy optimized by a genetic algorithm
A new strategy for global geometry optimization of clusters is presented.
Important features are a restriction of search space to favorable
nearest-neighbor distance ranges, a suitable cluster growth representation with
diminished correlations, and easy transferability of the results to larger
clusters. The strengths and possible limitations of the method are demonstrated
for Si10 using an empirical potential.Comment: accepted by Chem.Phys.Letters; 10 pages text, plus 3 pages for Title,
abstract, and figure caption; figures 1a and 1
Reliability-based design optimization of shells with uncertain geometry using adaptive Kriging metamodels
Optimal design under uncertainty has gained much attention in the past ten
years due to the ever increasing need for manufacturers to build robust systems
at the lowest cost. Reliability-based design optimization (RBDO) allows the
analyst to minimize some cost function while ensuring some minimal performances
cast as admissible failure probabilities for a set of performance functions. In
order to address real-world engineering problems in which the performance is
assessed through computational models (e.g., finite element models in
structural mechanics) metamodeling techniques have been developed in the past
decade. This paper introduces adaptive Kriging surrogate models to solve the
RBDO problem. The latter is cast in an augmented space that "sums up" the range
of the design space and the aleatory uncertainty in the design parameters and
the environmental conditions. The surrogate model is used (i) for evaluating
robust estimates of the failure probabilities (and for enhancing the
computational experimental design by adaptive sampling) in order to achieve the
requested accuracy and (ii) for applying a gradient-based optimization
algorithm to get optimal values of the design parameters. The approach is
applied to the optimal design of ring-stiffened cylindrical shells used in
submarine engineering under uncertain geometric imperfections. For this
application the performance of the structure is related to buckling which is
addressed here by means of a finite element solution based on the asymptotic
numerical method
Fine-Grain Iterative Compilation for WCET Estimation
Compiler optimizations, although reducing the execution times of programs, raise issues in static WCET estimation techniques and tools. Flow facts, such as loop bounds, may not be automatically found by static WCET analysis tools after aggressive code optimizations. In this paper, we explore the use of iterative compilation (WCET-directed program optimization to explore the optimization space), with the objective to (i) allow flow facts to be automatically found and (ii) select optimizations that result in the lowest WCET estimates. We also explore to which extent code outlining helps, by allowing the selection of different optimization options for different code snippets of the application
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Diversity is an important factor in evolutionary algorithms to prevent
premature convergence towards a single local optimum. In order to maintain
diversity throughout the process of evolution, various means exist in
literature. We analyze approaches to diversity that (a) have an explicit and
quantifiable influence on fitness at the individual level and (b) require no
(or very little) additional domain knowledge such as domain-specific distance
functions. We also introduce the concept of genealogical diversity in a broader
study. We show that employing these approaches can help evolutionary algorithms
for global optimization in many cases.Comment: GECCO '18: Genetic and Evolutionary Computation Conference, 2018,
Kyoto, Japa
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