Artificial Bee Colony Based on Adaptive Selection Probability

2020 ◽  
pp. 21-30
Author(s):  
Songyi Xiao ◽  
Hui Wang ◽  
Minyang Xu ◽  
Wenjun Wang
Keyword(s):  
Artificial Bee Colony ◽  
Selection Probability ◽  
Adaptive Selection ◽  
Bee Colony

scholarly journals Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 10 (9) ◽  
pp. 2990 ◽  
Author(s):  
Huijuan Zhang ◽  
Zi Xie ◽  
Hsiung-Cheng Lin ◽  
Shaoyong Li
Keyword(s):  
Artificial Bee Colony ◽  
Capacity Allocation ◽  
High Energy ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Stable Operation ◽  
Power Capacity ◽  
Selection Probability ◽  
Multi Objective ◽  
Bee Colony

Although the combined cooling, heating and power (CCHP) microgrid is feasible for achieving a high energy utilization efficiency, the fluctuation of energy sources, such as a photovoltaic system and multiple loads, may affect the safety, economics and stability in CCHP microgrid operation. For this reason, this paper establishes a mathematical model using a multi-objective optimization mechanism for resolving the influence of economy and energy allocation in the mixed photovoltaic type CCHP microgrid. It is based on analytic hierarchy process (AHP) to determine the individual weight of objective function optimization for the multi-objective power capacity allocation. The improved artificial bee colony (IABC) based on the whale search and dynamic selection probability can achieve an optimization solution, reaching a stable operation state and reasonable capacity configuration in the microgrid system. The performance results confirm that the proposed algorithm is superior to others in both convergence speed and accuracyfor the capacity allocation of the CCHP microgrid.

An Improved Artificial Bee Colony Algorithm for the Flexible Integrated Scheduling Problem Using Networked Devices Collaboration

2020 ◽  
Vol 29 (01n02) ◽  
pp. 2040003
Author(s):  
Zhiqiang Xie ◽  
Dan Yang ◽  
Mingrui Ma ◽  
Xu Yu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Selection Probability ◽  
Integrated Scheduling ◽  
Bee Colony ◽  
Structure Evaluation ◽  
Networked Devices

This paper studies the flexible integrated scheduling problem, which is an extension of job-shop scheduling considering the assembly and the machining stages at the same time, with networked devices (FISND). The completion time of the entire product may be impacted by the uncertainty of the process constraint structure and flexible equipment, so we take machining structure evaluation (MSE) into account. We proposed an improved artificial bee colony algorithm considering MSE (ABC[Formula: see text]) with two new strategies: one is a dynamic perturbation step strategy and the other is double-chain similarity and migration time factor strategy to evaluate the product processing structure, and then we computed the selection probability of the followers by it. Finally, the experimental results show that ABC[Formula: see text] has a better performance and faster convergence than the algorithm ABC, hyABC, GA. It can not only solve the flexible integrated scheduling problem with networked devices, but also yield better solutions to the typical flexible integrated scheduling problem than the flexible equipment integrated scheduling algorithm based on device-driven.

Dynamic Deployment of Wireless Sensor Networks by an Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 511-512 ◽  
pp. 862-866
Author(s):  
Pei Yu He ◽  
Ming Yan Jiang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Coverage Rate ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Selection Probability ◽  
Bee Colony ◽  
Dynamic Deployment

The main objective of dynamic deployment in Wireless Sensor Networks is to use the mobility of sensor nodes, trying to increase the coverage area of the networks through dynamically adjusting the node positions. In this paper, with the characteristics of the optimal sensor deployment in theory, we improve the standard artificial bee colony algorithm by introducing the distance factor to improve the onlooker bees selection probability formula, changing the working mode of the scout bee to avoid worse coverage rate and changing the limit l for scout bee to increase the chance to get better solution. Results show that the improved artificial bee colony algorithm has faster convergent speed and higher coverage rate compared with the standard artificial bee colony algorithm.

PERFORMANCE EVALUATION OF CULTURAL ARTIFICIAL BEE COLONY AND CULTURAL ARTIFICIAL FISH SWARM ALGORITHM

2019 ◽  
Vol 6 (4) ◽  
pp. 43
Author(s):  
HADIR ADEBIYI BUSAYO ◽  
TIJANI SALAWUDEEN AHMED ◽  
FOLASHADE O. ADEBIYI RISIKAT ◽  
◽  
◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Artificial Bee Colony ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

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