3,796 research outputs found
Orthogonal learning particle swarm optimization
Particle swarm optimization (PSO) relies on its
learning strategy to guide its search direction. Traditionally,
each particle utilizes its historical best experience and its neighborhood’s
best experience through linear summation. Such a
learning strategy is easy to use, but is inefficient when searching
in complex problem spaces. Hence, designing learning strategies
that can utilize previous search information (experience) more
efficiently has become one of the most salient and active PSO
research topics. In this paper, we proposes an orthogonal learning
(OL) strategy for PSO to discover more useful information that
lies in the above two experiences via orthogonal experimental
design. We name this PSO as orthogonal learning particle swarm
optimization (OLPSO). The OL strategy can guide particles to
fly in better directions by constructing a much promising and
efficient exemplar. The OL strategy can be applied to PSO with
any topological structure. In this paper, it is applied to both global
and local versions of PSO, yielding the OLPSO-G and OLPSOL
algorithms, respectively. This new learning strategy and the
new algorithms are tested on a set of 16 benchmark functions, and
are compared with other PSO algorithms and some state of the
art evolutionary algorithms. The experimental results illustrate
the effectiveness and efficiency of the proposed learning strategy
and algorithms. The comparisons show that OLPSO significantly
improves the performance of PSO, offering faster global convergence,
higher solution quality, and stronger robustness
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Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
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