A comparative study on binary Artificial Bee Colony optimization methods for feature selection

2016 ◽  
Author(s):  
Zeynep Banu Ozger ◽  
Bulent Bolat ◽  
Banu Diri
Keyword(s):  
Feature Selection ◽  
Comparative Study ◽  
Artificial Bee Colony ◽  
Optimization Methods ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

scholarly journals Pareto front feature selection based on artificial bee colony optimization

2018 ◽  
Vol 422 ◽  
pp. 462-479 ◽  
Author(s):  
Emrah Hancer ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
Dervis Karaboga ◽  
Bahriye Akay
Keyword(s):  
Feature Selection ◽  
Pareto Front ◽  
Artificial Bee Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Artificial Bee Colony Optimization for Feature Selection of Traffic Sign Recognition

2017 ◽  
Vol 8 (2) ◽  
pp. 50-66 ◽  
Author(s):  
Diogo L. da Silva ◽  
Leticia M. Seijas ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Feature Selection ◽  
High Performance ◽  
Artificial Bee Colony ◽  
Information Gain ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Sign Recognition ◽  
Bee Colony Optimization

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.

scholarly journals Artificial Bee Colony Optimization of NOx Emission and Reheat Steam Temperature in a 1000 MW Boiler

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Xian-hua Gao ◽  
Zhi-gang Su
Keyword(s):  
Artificial Bee Colony ◽  
Predictive Performance ◽  
Optimization Methods ◽  
Nox Emission ◽  
Steam Temperature ◽  
Nsga Ii ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Reheat Steam ◽  
Bee Colony Optimization

This paper puts forward a new viewpoint on optimization of boiler combustion, namely, reducing NOx emission while maintaining higher reheat steam temperature rather than reducing NOx emission while improving boiler efficiency like traditional practices. Firstly, a set of multioutputs nonlinear partial least squares (MO-NPLS) models are established as predictors to predict these two indicators. To guarantee better predictive performance, repeated double cross-validation (rdCV) strategy is proposed to identify the structure as well as parameters of the predictors. Afterward, some controllable process variables, taken as inputs of the predictors, are then optimized by minimizing NOx emission and maximizing reheat steam temperature via multiobjective artificial bee colony (MO-ABC). Results show that our rdCV-MO-NPLS model with MO-ABC optimization methods can reduce NOx emission synchronously and improve reheat steam temperature effectively compared with nondominated sorting genetic algorithm II (NSGA-II) and combustion adjustment experimental data on a real 1000 MW boiler.

scholarly journals Bičių spiečių imitavimas sprendžiant optimizavimo uždavinius

2011 ◽  
Vol 56 ◽  
pp. 163-173
Author(s):  
Alfonsas Misevičius ◽  
Jonas Blonskis ◽  
Vytautas Bukšnaitis
Keyword(s):  
Swarm Intelligence ◽  
Honey Bees ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Optimization Methods ◽  
Quadratic Assignment ◽  
Optimization Approach ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Straipsnyje nagrinėjami klausimai, susiję su naujoviškų metodų taikymu sprendžiant optimizavimo uždavinius. Šiuo konkrečiu atveju diskutuojama apie bičių spiečių elgsenos imitavimą ir galimą jo taikymą kombinatorinio (diskretinio) tipo optimizavimo uždaviniams. Straipsnio pradžioje aptariami konceptualūs aspektai ir bendroji bičių spiečių imitavimo algoritmų idėja. Aprašoma bičių spiečiaus imitavimo algoritmo realizacija atskiram nagrinėjamam atvejui – kvadratinio paskirstymo uždaviniui, kuris yra vienas iš aktualių ir sudėtingų kombinatorinio optimizavimo uždavinių pavyzdžių. Straipsnyje pateikiami ir su realizuotu algoritmu atliktų eksperimentų rezultatai, kurie iliustruoja skirtingų veiksnių (parametrų) įtaką gaunamų sprendinių kokybei ir patvirtina aukštą algoritmo efektyvumo lygį.Bee Swarm Intelligence in (Combinatorial) OptimizationAlfonsas Misevičius, Jonas Blonskis, Vytautas Bukšnaitis SummaryIn this paper, we discuss some issues related to the innovative intelligent optimization methods. More precisely, we are concerned with the bee colony optimization approach, which is inspired by the behaviour of natural swarms of honey bees. Both the conceptual methodological facets of the swarm intelligence paradigm and the aspects of implementation of the artificial bee colony algorithms are considered. In particular, we introduce an implementation of the artificial bee colony optimization algorithm for the well-known combinatorial optimization problem of quadratic assignment (QAP). The results of computational experiments with different variants of the implemented algorithm are also presented and discussed. Based on the obtained results, it is concluded that the proposed algorithm may compete with other efficient heuristic techniques. 

Mining of structural motifs in proteins using artificial bee colony optimization framework for druggability

2021 ◽  
Author(s):  
L. S. Suma ◽  
S. S. Vinod Chandra
Keyword(s):  
Artificial Bee Colony ◽  
Area Under The Curve ◽  
Docking Studies ◽  
Artificial Bee Colony Optimization ◽  
Optimization Framework ◽  
Bee Colony ◽  
Novel Variant ◽  
Bee Colony Optimization ◽  
Novel Strategy ◽  
Sensitivity Specificity

In this work, we have developed an optimization framework for digging out common structural patterns inherent in DNA binding proteins. A novel variant of the artificial bee colony optimization algorithm is proposed to improve the exploitation process. Experiments on four benchmark objective functions for different dimensions proved the speedier convergence of the algorithm. Also, it has generated optimum features of Helix Turn Helix structural pattern based on the objective function defined with occurrence count on secondary structure. The proposed algorithm outperformed the compared methods in convergence speed and the quality of generated motif features. The motif locations obtained using the derived common pattern are compared with the results of two other motif detection tools. 92% of tested proteins have produced matching locations with the results of the compared methods. The performance of the approach was analyzed with various measures and observed higher sensitivity, specificity and area under the curve values. A novel strategy for druggability finding by docking studies, targeting the motif locations is also discussed.

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