University course timetabling using hybridized artificial bee colony with hill climbing optimizer

Bolaji, Asaju La’aro; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.

doi:10.1016/j.jocs.2014.04.002

Cited by 58 publications

(31 citation statements)

References 25 publications

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“…food sources) locally to the maximum limit. The concept of this hybridization is adapted from our previous works on the ABC to the university timetabling problem [26] in which of the employed bee operator is replaced by the HCO in its operation to enhance the search intensification. Note that the feasibility of the solution search space is taken into consideration during the search process.…”

Section: Hybridization Abc With Hill Climbing Optimizermentioning

confidence: 99%

“…Awadallah et al [31] proposed other population-based metaheuristic method for NRP using the same INRC2010 dataset. Furthermore, the value of the second parameter limit is fixed, where this value is adopted from [26].…”

Section: Experimental Designmentioning

confidence: 99%

“…In particular, it has been successfully been adapted and hybridized with local search-based methods to empower its exploitation capability. Bolaji et al [26,27] proposed a hybrid approach combining ABC with the HCO for the university timetabling problems. In another research, the ABC is hybridized with a greedy heuristic and a local search for the quadratic knapsack problem [28].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A hybrid artificial bee colony for a nurse rostering problem

Awadallah

Bolaji

Al-Betar

2015

Applied Soft Computing

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Section: Hybridization Abc With Hill Climbing Optimizermentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid artificial bee colony for a nurse rostering problem

Awadallah

Bolaji

Al-Betar

2015

Applied Soft Computing

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“…While smaller instances might be solved by exact algorithms, most real-world problems are large dimensional problems, so there is a need for heuristic methods to obtain near-optimal solutions in reasonable time. The most used ones are metaheuristics like tabu search (Valdes, Crespo and Tamarit, 2002;Yuan and Lan, 2016), simulated annealing (Abramson, 1991;Thompson and Dowsland, 1998;Bellio, Ceschia, Di Gaspero, Schaerf and Urli, 2016;Goh, Kendall and Sabar 2018), genetic and evolutionary algorithms (Beligiannis, Moschopoulosa, Kaperonisa and Likothanassisa, 2008;Susan and Bhutani, 2018;Matias, Fajardo and Medina, 2018), neural networks (Kovačič, 1993), ant colonies (Socha et al, 2003), bee colony algorithm (Bolaji, Kahader and Betar, 2014), particle swarm optimization (Chen and Shih, 2013;Imran Hossain, Akhand, Shuvo, Siddique and Adeli,, 2019), artificial immune algorithm (Yazdani, Naderi and Zeinali, 2017) and hyperheuristics (Burke, McCollum, Meisels, Petrovic, and Qu, 2007b). Besides, there are some studies dealing with the analysis and design of interactive decision support system for timetable management (Piechowiak and Kolski, 2004;Kamisli Ozturk, Ozturk and Sagir, 2010).…”

Section: Problem Complexity and The Need For A Heuristic Approachmentioning

confidence: 99%

A New Mathematical Model and Random Key Based Metaheuristic Solution Approach for Course-Room-Time Assignment Problem

Öztürk

Sagir

2019

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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Course-room-time assignment problem, Mathematical modelling, Random key based genetic algorithms.This study presents a newly developed mixed-integer mathematical model for university course-room-time assignment problem. Optimal results with no soft constraint violations are obtained for some type of problem instances. As problem complexity increases it becomes more difficult to find feasible solution for this problem in a reasonable time. Therefore, a heuristic approach is often needed for such problems. In this study, a random key based genetic algorithm (RKGA) is developed. RKGA encoding is used in order to encode the chromosomes with a length of just the number of courses and not to use problem specific genetic operators and/or repair mechanisms. Well-known problem instances from the literature are selected to evaluate the outcome. The performance of RKGA is competitive to that of other algorithms especially for big size problems. Öz Ders-derslik-zaman dilimi atama problemi, Matematiksel modelleme, Rassal anahtar temelli genetik algoritma.Bu çalışmada üniversite ders-derslik-zaman dilimi atama problemi için yeni bir karma tam sayılı matematiksel model önerilmiştir. Geliştirilen karma tam sayılı matematiksel model ile literatürde yer alan test problemleri çözdürülmüş ve bir kısmı için tüm esnek kısıtlar sağlanarak en iyi çözüm elde edilmiştir. Problem karmaşıklığı arttıkça makul sürelerde uygun çözüm bulmak zorlaştığından, bu tür problemlerin çözümü için sezgisel bir yaklaşıma ihtiyaç duyulmaktadır. Çalışmada, rassal anahtar temelli bir genetik algoritma (RKGA) geliştirilmiştir. Probleme özgü özel genetik operatörler ve/veya onarma mekanizmaları kullanmamak için sadece ders sayısı uzunluğundaki kromozomları kodlamak için RKGA kodlaması kullanılmıştır. Çıktıların değerlendirilmesi için literatürde iyi bilinen test problemleri seçilmiştir. Özellikle büyük boyutlu problemlerde RKGA'nın performansının diğer algoritmalar ile rekabet edebilir düzeyde olduğu görülmüştür. Araştırma Makalesi defined as assigning courses to rooms and timeslots in such a way that there are no conflicts or clashes by satisfying objectives and the examination timetabling basically involves allocating a set of examinations to a set of rooms and time periods. The basic difference is that in course timetabling there cannot be more than one course per room in contrary of exam one. Since 1960's, when the timetabling problems have Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Sets and indices J = {j | j=1,…, n} for courses I = {i | i =1,…, m} for students K = {k | k = 1,…, h} for rooms L = {l | l = 1,… , o} for features T = {t | t =1,…, 45 } for timeslots D = {d | d =1,…, 5 } for days Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık

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“…The third group of solution method is the population-based optimization methods that gain more attention by researchers in seeking new methods that are inspired by the nature phenomena such as genetic algorithm [7,8,12], evolutionary algorithm [3,17,18], ant colony algorithm [19], bee colony algorithm [20,21], firefly algorithm [22], and particle swam optimization [23,24].…”

Section: Introductionmentioning

confidence: 99%

Global Optimization Using Local Search Approach for Course Scheduling Problem

Jamal¹

2020

Scheduling Problems - New Applications and Trends

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Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously.

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University course timetabling using hybridized artificial bee colony with hill climbing optimizer

Cited by 58 publications

References 25 publications

A hybrid artificial bee colony for a nurse rostering problem

A hybrid artificial bee colony for a nurse rostering problem

A New Mathematical Model and Random Key Based Metaheuristic Solution Approach for Course-Room-Time Assignment Problem

Global Optimization Using Local Search Approach for Course Scheduling Problem

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