3,745 research outputs found
Improved Fruit Fly Optimization Algorithm-based density peak clustering and its applications
Kao algoritam temeljen na gustoći, algoritam grupiranja na osnovu najviše gustoće (Density Peak Clustering - DPC) superioran je u grupiranju pronalaženjem vršne gustoće. No, smanjena udaljenost i središta grupiranja trebaju se postaviti slučajno, što bi utjecalo na rezultate grupiranja. Voćne mušice pronalaze najbolju hranu lokalnim pretraživanjem i globalnim pretraživanjem. Pronađena hrana je ekstremna vrijednost parametra izračunata algoritmom optimizacije voćne mušice (Fruit Fly Optimization Algorithm - FOA). Na osnovu brze pretrage i superiornosti brze konvergencije FOA-e, moguće je nadoknaditi slučajnost DPC-a. Poboljšana vršna gustoća grupiranja voćnih mušica, temeljena na algoritmu optimizacije, predložena je kao FOA-DPC. Taj bi algoritam trebao biti efikasniji i učinkovitiji od DPC algoritma. Rezultati sedam simulacijskih eksperimenata na UCI nizovima podataka potvrdili su da predloženi algoritam nije imao samo bolju performansu grupiranja već je bio bliži pravim brojevima grupiranja. Nadalje, FOA-DPC primijenjen je i u analizi financijskih podataka i pokazao se vrlo učinkovitim.As density-based algorithm, Density Peak Clustering (DPC) algorithm has superiority of clustering by finding the density peaks. But the cut-off distance and clustering centres had to be set at random, which would influence clustering outcomes. Fruit flies find the best food by local searching and global searching. The food found was the parameter extreme value calculated by Fruit Fly Optimization Algorithm (FOA). Based on the rapid search and fast convergence superiorities of FOA, it is possible to make up the casualness of DPC. An improved fruit fly optimization-based density peak clustering algorithm was proposed as FOA-DPC. The FOA-DPC algorithm would be more efficient and effective than DPC algorithm. The results of seven simulation experiments in UCI data sets validated that the proposed algorithm did not only have better clustering performance, but also were closer to the true clustering numbers. Furthermore, FOA-DPC was applied to practical financial data analysis and the conclusion was also effective
Combinatorial persistency criteria for multicut and max-cut
In combinatorial optimization, partial variable assignments are called
persistent if they agree with some optimal solution. We propose persistency
criteria for the multicut and max-cut problem as well as fast combinatorial
routines to verify them. The criteria that we derive are based on mappings that
improve feasible multicuts, respectively cuts. Our elementary criteria can be
checked enumeratively. The more advanced ones rely on fast algorithms for upper
and lower bounds for the respective cut problems and max-flow techniques for
auxiliary min-cut problems. Our methods can be used as a preprocessing
technique for reducing problem sizes or for computing partial optimality
guarantees for solutions output by heuristic solvers. We show the efficacy of
our methods on instances of both problems from computer vision, biomedical
image analysis and statistical physics
Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm
Bidirectional inductive power transfer (BIPT) system facilitates contactless power transfer between two sides and across an air-gap, through weak magnetic coupling. Typically, this system is nonlinear high order system which includes nonlinear switch components and resonant networks, developing of accurate model is a challenging task. In this paper, a novel technique for parameter identification of a BIPT system is presented by using chaotic-enhanced fruit fly optimization algorithm (CFOA). The fruit fly optimization algorithm (FOA) is a new meta-heuristic technique based on the swarm behavior of the fruit fly. This paper proposes a novel CFOA, which employs chaotic sequence to enhance the global optimization capacity of original FOA. The parameter identification of the BIPT system is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured values. All the 11 parameters of this system (Lpi, LT, Lsi, Lso, CT, Cs, M, Rpi, RT, Rsi and Rso) can be identified simultaneously using measured input–output data. Simulations show that the proposed parameter identification technique is robust to measurements noise and variation of operation condition and thus it is suitable for practical application
Finding mesoscopic communities in sparse networks
We suggest a fast method to find possibly overlapping network communities of
a desired size and link density. Our method is a natural generalization of the
finite- superparamegnetic Potts clustering introduced by Blatt, Wiseman, and
Domany (Phys. Rev. Lett. v.76, 3251 (1996) and the recently suggested by
Reichard and Bornholdt (Phys. Rev. Lett. v.93, 21870 (2004)) annealing of Potts
model with global antiferromagnetic term. Similarly to both preceding works,
the proposed generalization is based on ordering of ferromagnetic Potts model;
the novelty of the proposed approach lies in the adjustable dependence of the
antiferromagnetic term on the population of each Potts state, which
interpolates between the two previously considered cases. This adjustability
allows to empirically tune the algorithm to detect the maximum number of
communities of the given size and link density. We illustrate the method by
detecting protein complexes in high-throughput protein binding networks.Comment: 8 pages, 2 figure, typos corrected, 1 figure adde
Penalized Clustering of Large Scale Functional Data with Multiple Covariates
In this article, we propose a penalized clustering method for large scale
data with multiple covariates through a functional data approach. In the
proposed method, responses and covariates are linked together through
nonparametric multivariate functions (fixed effects), which have great
flexibility in modeling a variety of function features, such as jump points,
branching, and periodicity. Functional ANOVA is employed to further decompose
multivariate functions in a reproducing kernel Hilbert space and provide
associated notions of main effect and interaction. Parsimonious random effects
are used to capture various correlation structures. The mixed-effect models are
nested under a general mixture model, in which the heterogeneity of functional
data is characterized. We propose a penalized Henderson's likelihood approach
for model-fitting and design a rejection-controlled EM algorithm for the
estimation. Our method selects smoothing parameters through generalized
cross-validation. Furthermore, the Bayesian confidence intervals are used to
measure the clustering uncertainty. Simulation studies and real-data examples
are presented to investigate the empirical performance of the proposed method.
Open-source code is available in the R package MFDA
Multi-resolution Tensor Learning for Large-Scale Spatial Data
High-dimensional tensor models are notoriously computationally expensive to
train. We present a meta-learning algorithm, MMT, that can significantly speed
up the process for spatial tensor models. MMT leverages the property that
spatial data can be viewed at multiple resolutions, which are related by
coarsening and finegraining from one resolution to another. Using this
property, MMT learns a tensor model by starting from a coarse resolution and
iteratively increasing the model complexity. In order to not "over-train" on
coarse resolution models, we investigate an information-theoretic fine-graining
criterion to decide when to transition into higher-resolution models. We
provide both theoretical and empirical evidence for the advantages of this
approach. When applied to two real-world large-scale spatial datasets for
basketball player and animal behavior modeling, our approach demonstrate 3 key
benefits: 1) it efficiently captures higher-order interactions (i.e., tensor
latent factors), 2) it is orders of magnitude faster than fixed resolution
learning and scales to very fine-grained spatial resolutions, and 3) it
reliably yields accurate and interpretable models
Predictability and hierarchy in Drosophila behavior
Even the simplest of animals exhibit behavioral sequences with complex
temporal dynamics. Prominent amongst the proposed organizing principles for
these dynamics has been the idea of a hierarchy, wherein the movements an
animal makes can be understood as a set of nested sub-clusters. Although this
type of organization holds potential advantages in terms of motion control and
neural circuitry, measurements demonstrating this for an animal's entire
behavioral repertoire have been limited in scope and temporal complexity. Here,
we use a recently developed unsupervised technique to discover and track the
occurrence of all stereotyped behaviors performed by fruit flies moving in a
shallow arena. Calculating the optimally predictive representation of the fly's
future behaviors, we show that fly behavior exhibits multiple time scales and
is organized into a hierarchical structure that is indicative of its underlying
behavioral programs and its changing internal states
Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions
Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen
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