scholarly journals A Hybrid Artificial Bee Colony Algorithm for the Service Selection Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Changsheng Zhang ◽  
Bin Zhang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Service Selection ◽  
Search Space ◽  
Selection Problem ◽  
Foraging Strategies ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Related Service ◽  
Bee Colony Optimization

To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm calledh-ABC is proposed, which incorporates the ant colony optimization mechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the search space based on a clustering process. Then, based on this construct graph, different foraging strategies are designed for different groups of bees in the swarm. Finally, this approach is evaluated experimentally using different standard real datasets and synthetically generated datasets and compared with some recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solutions.

scholarly journals Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Maowei He ◽  
Kunyuan Hu ◽  
Yunlong Zhu ◽  
Lianbo Ma ◽  
Hanning Chen ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Information Exchange ◽  
Artificial Bee Colony ◽  
Complete Solution ◽  
Experimental Results ◽  
Divide And Conquer ◽  
Foraging Strategies ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.

scholarly journals A Connectivity Weighting DV_Hop Localization Algorithm Using Modified Artificial Bee Colony Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Tianfei Chen ◽  
Lijun Sun
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Sensor Nodes ◽  
Local Network ◽  
Localization Algorithm ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
True Value ◽  
Bee Colony Optimization ◽  
Anchor Nodes

Node localization is a fundamental issue in wireless sensor network (WSN), as many applications depend on the precise location of the sensor nodes (SNs). Among all localization algorithms, DV_Hop is a typical range-free localization algorithm characterized by such advantages as simple realization and low energy cost. From detailed analysis of localization error for the basic DV_Hop algorithm, we propose a connectivity weighting DV_Hop localization algorithm using modified artificial bee colony optimization. Firstly, the proposed algorithm calculates the average hop distance (AHD) of anchor nodes in terms of the minimum mean squared distance error between the estimated distances of anchor nodes and the corresponding actual distances from them. After that, a connectivity weighting method, considering the influence from both local network properties of anchor nodes and the distances from anchor nodes to unknown nodes, is designed to obtain the AHD of unknown nodes. In addition, we set up the weighting calculation proportion of anchor nodes at the same time. Finally, a modified artificial bee colony algorithm which enlarges searching space is used to optimize the execution of multilateral localization. The experimental results demonstrate that the connectivity weighting approach has better localization effect, and the AHD of unknown nodes close to true value can be obtained at a relatively large probability. Moreover, the modified artificial bee colony algorithm can reduce the probability of premature convergence, and thus the localization accuracy is further improved.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY CHAOTIC SEARCH BASED ARTIFICIAL BEE COLONY ALGORITHM

2018 ◽  
Vol 6 (1) ◽  
pp. 377-388
Author(s):  
K. Lenin
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Premature Convergence ◽  
Artificial Bee Colony Optimization ◽  
Chaotic Search ◽  
Bee Colony ◽  
Optimal Value ◽  
Bee Colony Optimization ◽  
Power Problem

This paper presents Chaotic Search Based Artificial Bee Colony Optimization Algorithm (CSABC) for solving the optimal reactive power problem. Basic Artificial Bee Colony algorithm (ABC) has the advantages of strong robustness, fast convergence and high flexibility, fewer setting parameters, but it has the disadvantages premature convergence in the later search period and the accuracy of the optimal value which cannot meet the requirements sometimes. In this paper the Chaotic Local Search method is applied to solve the reactive power problem of global optimal value. The premature convergence issue of the Artificial Bee Colony algorithm has been improved by increasing the number of scout and rational using of the global optimal value and Chaotic Search. The proposed Chaotic Search Based Artificial Bee Colony Optimization (CSABC) algorithm has been tested in stand IEEE 30, 118- bus & practical 191 Indian utility test systems. The results show that the proposed algorithm performs well in reducing the real power loss and prevent premature convergence to high degree with rapid convergence.

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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