scholarly journals Novel Bee Colony Optimization with Update Quantities for OFDMA Resource Allocation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ming Sun ◽  
Yujing Huang ◽  
Shumei Wang ◽  
Yaoqun Xu
Keyword(s):  
Resource Allocation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Subcarrier Allocation ◽  
Spectrum Utilization ◽  
Bee Colony ◽  
Data Rates ◽  
Nectar Sources ◽  
Bee Colony Optimization ◽  
Equal Power

In recent years, large usage of wireless networks puts forward challenge to the utilization of spectrum resources, and it is significant to improve the spectrum utilization and the system sum data rates in the premise of fairness. However, the existing algorithms have drawbacks in efficiency to maximize the sum data rates of orthogonal frequency division multiple access (OFDMA) systems in the premise of fairness threshold. To address the issue, a novel artificial bee colony algorithm with update quantities of nectar sources is proposed for OFDMA resource allocation in this paper. Firstly, the population of nectar sources is divided into several groups, and a different update quantity of nectar sources is set for each group. Secondly, based on the update quantities of nectar sources set for these groups, nectar sources are initialized by a greedy subcarrier allocation method. Thirdly, neighborhood searches and updates are performed on dimensions of nectar sources corresponding to the preset update quantities. The proposed algorithm can not only make the initialized nectar sources maintain high levels of fairness through the greedy subcarrier allocation but also use the preset update quantities to reduce dimensions of the nectar sources to be optimized by the artificial bee colony algorithm, thereby making full use of both the local optimization of the greedy method and the global optimization of the artificial bee colony algorithm. The simulation results show that, just in the equal-power subcarrier allocation stage, the proposed algorithm can achieve the required fairness threshold and effectively improve the system sum data rates.

Artificial Bee Colony Algorithm for Traveling Salesman Problem

2011 ◽  
Vol 314-316 ◽  
pp. 2191-2196 ◽  
Author(s):  
Wei Hua Li ◽  
Wei Jia Li ◽  
Yuan Yang ◽  
Hai Qiang Liao ◽  
Ji Long Li ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Nearest Neighbor ◽  
Traveling Salesman ◽  
Comparison Study ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Benchmark Instances ◽  
Bee Colony Optimization

By combining the modified nearest neighbor approach and the improved inver-over operation, an Artificial Bee Colony (ABC) Algorithm for Traveling Salesman Problem (TSP) is proposed in this paper. The heuristic approach was tested in some benchmark instances selected from TSPLIB. In addition, a comparison study between the proposed algorithm and the Bee Colony Optimization (BCO) model is presented. Experimental results show that the presented algorithm outperforms the BCO method and can efficiently tackle the small and medium scale TSP instances.

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 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.

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