scholarly journals School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 73
Author(s):  
Kaixiang Zhu ◽  
Lily D. Li ◽  
Michael Li
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Bee Colony ◽  
Educational Timetabling ◽  
Proposed Model ◽  
School Timetabling ◽  
School Timetabling Problem ◽  
World School

Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators’ availabilities, preferences, and expertise levels.

scholarly journals School Timetabling Optimisation Using Artificial Bee Colony Algorithm Based on a Virtual Searching Space Method

2021 ◽  
Author(s):  
Kaixiang Zhu ◽  
Lily D Li ◽  
Michael Li
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Computer Hardware ◽  
Large Dataset ◽  
Bee Colony ◽  
Educational Timetabling ◽  
Proposed Model ◽  
School Timetabling ◽  
School Timetabling Problem

Although educational timetabling problems have been studied for decades, one type, the STP, has not developed as quickly as the other two types due to its diversity and complexity. Also, most of the STP research has only focused on the educators’ availabilities when studying the educator aspect, and the educators’ preferences and expertise have not been taken into consideration. This paper proposes a conceptual model for the school timetabling problem considering educators’ availabilities, preferences and expertise as a whole, and chooses a common real-world school timetabling scenario to study. A mathematical model is presented. A Virtual search space for dealing with the large search space is introduced, and the artificial bee colony algorithm is adapted and applied to the proposed model. The proposed approach is simulated with a random-generated large dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computer hardware environment, which significantly reduces computational costs. Compared to the traditional CP method, the proposed approach is more effective and can provide more satisfactory solutions in considering educators’ availabilities, preferences, and expertise levels.

Artificial Bee Colony Algorithm for Solving Educational Timetabling Problems

2012 ◽  
Vol 3 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Solution Method ◽  
Search Space ◽  
Initial Population ◽  
Timetabling Problem ◽  
Bee Colony ◽  
Educational Timetabling ◽  
Problem Instances ◽  
Uncapacitated Examination Timetabling Problem

This paper presents an artificial bee colony algorithm (ABC) for Education Timetabling Problem (ETP). It is aimed at developing a good-quality solution for the problem. The initial population of solutions was generated using Saturation Degree (SD) and Backtracking Algorithm (BA) to ensure the feasibility of the solutions. At the improvement stage in the solution method, ABC uses neighbourhood structures iteratively within the employed and onlooker bee operators, in order to rigorously navigate the UTP search space. The technique was evaluated using curriculum-based course timetabling (CB-CTT) and Uncapacitated Examination Timetabling Problem (UETP) problem instances. The experimental results on UETP showed that the technique is comparable with other state-of-the-art techniques and provides encouraging results on CB-CTT.

An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

2021 ◽  
pp. 1-16
Author(s):  
Ghizlane Khababa ◽  
Fateh Seghir ◽  
Sadik Bessou
Keyword(s):  
Web Services ◽  
Local Search ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Evolutionary Strategy ◽  
Extended Version ◽  
The Public ◽  
Bee Colony ◽  
Skyline Operator ◽  
Experimental Comparisons

 In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.

Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

2021 ◽  
pp. 1203-1221
Author(s):  
Shivlal Mewada ◽  
Sita Sharan Gautam ◽  
Pradeep Sharma
Keyword(s):  
Privacy Preservation ◽  
Artificial Bee Colony ◽  
Model Simulation ◽  
Privacy Preserving ◽  
Global Network ◽  
Sensitive Information ◽  
Healthcare Applications ◽  
Efficient Manner ◽  
Bee Colony ◽  
Proposed Model

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

A Modified Artificial Bee Colony Algorithm for Post-enrolment Course Timetabling

2013 ◽  
pp. 377-386 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Course Timetabling ◽  
Bee Colony
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