37,688 research outputs found
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
Learning Opposites with Evolving Rules
The idea of opposition-based learning was introduced 10 years ago. Since then
a noteworthy group of researchers has used some notions of oppositeness to
improve existing optimization and learning algorithms. Among others,
evolutionary algorithms, reinforcement agents, and neural networks have been
reportedly extended into their opposition-based version to become faster and/or
more accurate. However, most works still use a simple notion of opposites,
namely linear (or type- I) opposition, that for each assigns its
opposite as . This, of course, is a very naive estimate of
the actual or true (non-linear) opposite , which has been
called type-II opposite in literature. In absence of any knowledge about a
function that we need to approximate, there seems to be no
alternative to the naivety of type-I opposition if one intents to utilize
oppositional concepts. But the question is if we can receive some level of
accuracy increase and time savings by using the naive opposite estimate
according to all reports in literature, what would we be able to
gain, in terms of even higher accuracies and more reduction in computational
complexity, if we would generate and employ true opposites? This work
introduces an approach to approximate type-II opposites using evolving fuzzy
rules when we first perform opposition mining. We show with multiple examples
that learning true opposites is possible when we mine the opposites from the
training data to subsequently approximate .Comment: Accepted for publication in The 2015 IEEE International Conference on
Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke
Observational Constraints on the Catastrophic Disruption Rate of Small Main Belt Asteroids
We have calculated 90% confidence limits on the steady-state rate of
catastrophic disruptions of main belt asteroids in terms of the absolute
magnitude at which one catastrophic disruption occurs per year (HCL) as a
function of the post-disruption increase in brightness (delta m) and subsequent
brightness decay rate (tau). The confidence limits were calculated using the
brightest unknown main belt asteroid (V = 18.5) detected with the Pan-STARRS1
(Pan-STARRS1) telescope. We measured the Pan-STARRS1's catastrophic disruption
detection efficiency over a 453-day interval using the Pan-STARRS moving object
processing system (MOPS) and a simple model for the catastrophic disruption
event's photometric behavior in a small aperture centered on the catastrophic
disruption event. Our simplistic catastrophic disruption model suggests that
delta m = 20 mag and 0.01 mag d-1 < tau < 0.1 mag d-1 which would imply that H0
= 28 -- strongly inconsistent with H0,B2005 = 23.26 +/- 0.02 predicted by
Bottke et al. (2005) using purely collisional models. We postulate that the
solution to the discrepancy is that > 99% of main belt catastrophic disruptions
in the size range to which this study was sensitive (100 m) are not
impact-generated, but are instead due to fainter rotational breakups, of which
the recent discoveries of disrupted asteroids P/2013 P5 and P/2013 R3 are
probable examples. We estimate that current and upcoming asteroid surveys may
discover up to 10 catastrophic disruptions/year brighter than V = 18.5.Comment: 61 Pages, 10 Figures, 3 Table
Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump
Measuring and forecasting opinion trends from real-time social media is a
long-standing goal of big-data analytics. Despite its importance, there has
been no conclusive scientific evidence so far that social media activity can
capture the opinion of the general population. Here we develop a method to
infer the opinion of Twitter users regarding the candidates of the 2016 US
Presidential Election by using a combination of statistical physics of complex
networks and machine learning based on hashtags co-occurrence to develop an
in-domain training set approaching 1 million tweets. We investigate the social
networks formed by the interactions among millions of Twitter users and infer
the support of each user to the presidential candidates. The resulting Twitter
trends follow the New York Times National Polling Average, which represents an
aggregate of hundreds of independent traditional polls, with remarkable
accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls
by 10 days, showing that Twitter can be an early signal of global opinion
trends. Our analytics unleash the power of Twitter to uncover social trends
from elections, brands to political movements, and at a fraction of the cost of
national polls
Stability and chaos in coupled two-dimensional maps on Gene Regulatory Network of bacterium E.Coli
The collective dynamics of coupled two-dimensional chaotic maps on complex
networks is known to exhibit a rich variety of emergent properties which
crucially depend on the underlying network topology. We investigate the
collective motion of Chirikov standard maps interacting with time delay through
directed links of Gene Regulatory Network of bacterium Escherichia Coli.
Departures from strongly chaotic behavior of the isolated maps are studied in
relation to different coupling forms and strengths. At smaller coupling
intensities the network induces stable and coherent emergent dynamics. The
unstable behavior appearing with increase of coupling strength remains confined
within a connected sub-network. For the appropriate coupling, network exhibits
statistically robust self-organized dynamics in a weakly chaotic regime
Pork Barrel Politics in Postwar Italy, 1953-1994
This paper analyzes the political determinants of the distribution of infrastructure expenditures by the Italian government to the country’s 92 provinces between 1953 and 1994. Extending implications of theories of legislative behavior to the context of open-list proportional representation, we examine whether individually powerful legislators and ruling parties direct spending to core or marginal electoral districts, and whether opposition parties share resources via a norm of universalism. We show that when districts elect politically more powerful deputies from the governing parties, they receive more investments. We interpret this as indicating that legislators with political resources reward their core voters by investing in public works in their districts. The governing parties, by contrast, are not able to discipline their own members of parliament sufficiently to target the parties’ areas of core electoral strength. Finally, we find no evidence that a norm of universalism operates to steer resources to areas when the main opposition party gains more votes
Efficient intra- and inter-night linking of asteroid detections using kd-trees
The Panoramic Survey Telescope And Rapid Response System (Pan-STARRS) under
development at the University of Hawaii's Institute for Astronomy is creating
the first fully automated end-to-end Moving Object Processing System (MOPS) in
the world. It will be capable of identifying detections of moving objects in
our solar system and linking those detections within and between nights,
attributing those detections to known objects, calculating initial and
differentially-corrected orbits for linked detections, precovering detections
when they exist, and orbit identification. Here we describe new kd-tree and
variable-tree algorithms that allow fast, efficient, scalable linking of intra
and inter-night detections. Using a pseudo-realistic simulation of the
Pan-STARRS survey strategy incorporating weather, astrometric accuracy and
false detections we have achieved nearly 100% efficiency and accuracy for
intra-night linking and nearly 100% efficiency for inter-night linking within a
lunation. At realistic sky-plane densities for both real and false detections
the intra-night linking of detections into `tracks' currently has an accuracy
of 0.3%. Successful tests of the MOPS on real source detections from the
Spacewatch asteroid survey indicate that the MOPS is capable of identifying
asteroids in real data.Comment: Accepted to Icaru
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