Artificial Bee Colony Algorithm for the Parallel Test Tasks Scheduling

2012 ◽  
Vol 591-593 ◽  
pp. 2478-2481
Author(s):  
Lian Chen ◽  
Ming Qing Xiao ◽  
Hang Yu ◽  
Liang Liang Zhao
Keyword(s):  
Mathematical Model ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Scheduling ◽  
Test Scheduling ◽  
Key Technology ◽  
Parallel Test ◽  
Bee Colony ◽  
Tasks Scheduling ◽  
The Mathematical Model

Parallel test is the key technology of the NxTest technology and the parallel test tasks scheduling is one of the important part of parallel test. The mathematical model of the problem was introduced, according to the advantage of solving the problem of dynamic scheduling with artificial bee colony algorithm; an approach of parallel test scheduling based on artificial bee colony algorithm is brought forward. An example was given, the result of simulation shows that this algorithm’s constringency fast and the result has a high precision, it is an efficient way of solving the problem of optimized parallel test tasks scheduling.

scholarly journals A Discrete Multi Objective Artificial Bee Colony Algorithm for a Real World Electronic Device Testing Machine Allocation Problem

2021 ◽  
Author(s):  
Jin Xie ◽  
Xinyu Li ◽  
Liang GAO
Keyword(s):  
Mathematical Model ◽  
Real World ◽  
Artificial Bee Colony ◽  
Electronic Device ◽  
Testing Machine ◽  
Allocation Problem ◽  
Multi Objective ◽  
Bee Colony ◽  
Machine Allocation ◽  
The Mathematical Model

Abstract This paper studies the Electronic Device Testing Machine Allocation Problem (EDTMAP), aiming to improve the production of electronic devices and reduce the scheduling distance of testing machines through reasonable machine allocation. Firstly, a mathematical model is formulated for the EDTMAP to maximize both production and the opposite of the scheduling distance of testing machines. Secondly, we develop a Discrete Multi Objective Artificial Bee Colony (DMOABC) algorithm to solve the EDTMAP. A crossover operator and a local search operator are designed to improve the exploration and exploitation of the algorithm, respectively. Some numerical experiments are designed to evaluate the performance of the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm compared with NSGA --Ⅱ and SPEA2. Finally, the mathematical model and the DMOABC algorithm areapplied to a real world factory that tests radio frequency modules. The result also verifies our method can significantly improve production and reduce the scheduling distance of testing machines.

scholarly journals A timetable optimization model and an improved artificial bee colony algorithm for maximizing regenerative energy utilization in a subway system

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879703 ◽  
Author(s):  
Hongjie Liu ◽  
Tao Tang ◽  
Xiwang Guo ◽  
Xisheng Xia
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Energy Utilization ◽  
Time Duration ◽  
Bee Colony ◽  
Subway System ◽  
Regenerative Energy ◽  
Timetable Optimization

Maximizing regenerative energy utilization in subway systems has become a hot research topic in recent years. By coordinating traction and braking trains in a substation, regenerative energy is optimally utilized and thus energy consumption from the substation can be reduced. This article proposes a timetable optimization problem to maximize regenerative energy utilization in a subway system with headway and dwell time control. We formulate its mathematical model, and some required constraints are considered in the model. To keep the operation time duration constant, the headway time between different trains can be different. An improved artificial bee colony algorithm is designed to solve the problem. Its main procedure and some related tasks are presented. Numerical experiments based on the data from a subway line in China are conducted, and improved artificial bee colony is compared with a genetic algorithm. Experimental results prove the correctness of the mathematical model and the effectiveness of improved artificial bee colony, which improves regenerative energy utilization for the experimental line and performs better than genetic 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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