318,733 research outputs found
Binary Particle Swarm Optimization based Biclustering of Web usage Data
Web mining is the nontrivial process to discover valid, novel, potentially
useful knowledge from web data using the data mining techniques or methods. It
may give information that is useful for improving the services offered by web
portals and information access and retrieval tools. With the rapid development
of biclustering, more researchers have applied the biclustering technique to
different fields in recent years. When biclustering approach is applied to the
web usage data it automatically captures the hidden browsing patterns from it
in the form of biclusters. In this work, swarm intelligent technique is
combined with biclustering approach to propose an algorithm called Binary
Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The
main objective of this algorithm is to retrieve the global optimal bicluster
from the web usage data. These biclusters contain relationships between web
users and web pages which are useful for the E-Commerce applications like web
advertising and marketing. Experiments are conducted on real dataset to prove
the efficiency of the proposed algorithms
Constructing IGA-suitable planar parameterization from complex CAD boundary by domain partition and global/local optimization
In this paper, we propose a general framework for constructing IGA-suitable
planar B-spline parameterizations from given complex CAD boundaries consisting
of a set of B-spline curves. Instead of forming the computational domain by a
simple boundary, planar domains with high genus and more complex boundary
curves are considered. Firstly, some pre-processing operations including
B\'ezier extraction and subdivision are performed on each boundary curve in
order to generate a high-quality planar parameterization; then a robust planar
domain partition framework is proposed to construct high-quality patch-meshing
results with few singularities from the discrete boundary formed by connecting
the end points of the resulting boundary segments. After the topology
information generation of quadrilateral decomposition, the optimal placement of
interior B\'ezier curves corresponding to the interior edges of the
quadrangulation is constructed by a global optimization method to achieve a
patch-partition with high quality. Finally, after the imposition of
C1=G1-continuity constraints on the interface of neighboring B\'ezier patches
with respect to each quad in the quadrangulation, the high-quality B\'ezier
patch parameterization is obtained by a C1-constrained local optimization
method to achieve uniform and orthogonal iso-parametric structures while
keeping the continuity conditions between patches. The efficiency and
robustness of the proposed method are demonstrated by several examples which
are compared to results obtained by the skeleton-based parameterization
approach
The True Destination of EGO is Multi-local Optimization
Efficient global optimization is a popular algorithm for the optimization of
expensive multimodal black-box functions. One important reason for its
popularity is its theoretical foundation of global convergence. However, as the
budgets in expensive optimization are very small, the asymptotic properties
only play a minor role and the algorithm sometimes comes off badly in
experimental comparisons. Many alternative variants have therefore been
proposed over the years. In this work, we show experimentally that the
algorithm instead has its strength in a setting where multiple optima are to be
identified
Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses
This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design
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
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Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution
D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem
Optimized Superconducting Nanowire Single Photon Detectors to Maximize Absorptance
Dispersion characteristics of four types of superconducting nanowire single
photon detectors, nano-cavity-array- (NCA-), nano-cavity-deflector-array-
(NCDA-), nano-cavity-double-deflector-array- (NCDDA-) and
nano-cavity-trench-array- (NCTA-) integrated (I-A-SNSPDs) devices was optimized
in three periodicity intervals commensurate with half-, three-quarter- and one
SPP wavelength. The optimal configurations capable of maximizing NbN
absorptance correspond to periodicity dependent tilting in S-orientation
(90{\deg} azimuthal orientation). In NCAI-A-SNSPDs absorptance maxima are
reached at the plasmonic Brewster angle (PBA) due to light tunneling. The
absorptance maximum is attained in a wide plasmonic-pass-band in
NCDAI_1/2*lambda-A, inside a flat-plasmonic-pass-band in NCDAI_3/4*lambda-A and
inside a narrow plasmonic-band in NCDAI_lambda-A. In NCDDAI_1/2*lambda-A bands
of strongly-coupled cavity and plasmonic modes cross, in NCDDAI_3/4*lambda-A an
inverted-plasmonic-band-gap develops, while in NCDDAI_lambda-A a narrow
plasmonic-pass-band appears inside an inverted-minigap. The absorptance maximum
is achieved in NCTAI_1/2*lambda-A inside a plasmonic-pass-band, in
NCTAI_3/4*lambda-A at inverted-plasmonic-band-gap center, while in
NCTAI_lambda-A inside an inverted-minigap. The highest 95.05% absorptance is
attained at perpendicular incidence onto NCTAI_lambda-A. Quarter-wavelength
type cavity modes contribute to the near-field enhancement around NbN segments
except in NCDAI_lambda-A and NCDDAI_3/4*lambda-A. The polarization contrast is
moderate in NCAI-A-SNSPDs (~10^2), NCDAI- and NCDDAI-A-SNSPDs make possible to
attain considerably large polarization contrast (~10^2-10^3 and ~10^3-10^4),
while NCTAI-A-SNSPDs exhibit a weak polarization selectivity (~10-10^2).Comment: 26 pages, 8 figure
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