A novel artificial bee colony optimization strategy-based extreme learning machine algorithm

2016 ◽  
Vol 6 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Yang Wang ◽  
Anna Wang ◽  
Qing Ai ◽  
Haijing Sun
Keyword(s):  
Extreme Learning Machine ◽  
Artificial Bee Colony ◽  
Optimization Strategy ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Learning Machine

scholarly journals An Adaptive Hybrid Ant Colony Optimization Algorithm for The Classification Problem

2019 ◽  
Vol 48 (4) ◽  
pp. 590-601
Author(s):  
Anxiang Ma ◽  
Xiaohong Zhang ◽  
Changsheng Zhang ◽  
Bin Zhang
Keyword(s):  
Artificial Bee Colony ◽  
Classification Problem ◽  
Ant Colony ◽  
Classification Rules ◽  
Optimization Strategy ◽  
Ant Colony Optimization Algorithm ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Rule Evaluation

Classification is an important data analysis and data mining technique. Taking into account the comprehensibility of the classifier generated, an adaptive hybrid ant colony optimization algorithm called A_HACO is proposed which can effectively solve classification problem and get the comprehensible classification rules at the same time. The algorithm incorporates the artificial bee colony optimization strategy into the ant colony algorithm. The ant colony global optimization process is used to adaptively select the appropriate rule evaluation function for the data set given. Based on the classification rules obtained, the artificial bee colony optimization strategy is used to tackle the continuous attributes for further optimization of classification rules. This approach is evaluated experimentally using different standard real datasets, and compared with some proposed related classification algorithms. It shows that A_HACO can adaptively select the appropriate rule evaluation function and has better accuracy compared with related works.

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.

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
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