Modified and hybridised bi-objective firefly algorithms for university course scheduling

Optimization in University Student Timetables: A Comprehensive Literature Review

“…Figure 1 below shows the flow diagram for the comprehensive review process. Reference Title Year Country [11] Developing a mobile-based application system to accelerate the efficiency of the course rescheduling process Malaysia [12] Modified and hybridised bi-objective firefly algorithms for university course scheduling Thailand [13] A general mathematical model for university courses timetabling: Implementation to a public university in Malaysia Malaysia [14] A genetic algorithm for the real-world university course timetabling problem Malaysia [15] Grouping and heuristics for a multi-stage class timetabling system Malaysia [16] Hybrid whale optimization algorithm for solving timetabling problems of ITC 2019 Indonesia [17] Investigation of heuristic orderings with a perturbation for finding feasibility in solving real-world university course timetabling problem Malaysia [18] Lecturer-course assignment model in national joint courses program to improve education quality and lecturers' time preference Indonesia [19] A compromise programming for multi-objective task assignment problem Vietnam [20] A hybrid of heuristic orderings and variable neighbourhood descent for a real-life university course timetabling problem Malaysia [21] An SHO-based approach to timetable scheduling: a case study Vietnam [22] Application of genetic algorithm to optimize lecture scheduling based on lecturers' teaching day willingness Indonesia [23] Automation and optimization of course timetabling using the iterated local search hyper-heuristic algorithm with the problem domain from the 2019 international timetabling competition Indonesia [24] Class scheduling framework using decorator and facade design pattern Philippines [25] Effective solution of university course timetabling using particle swarm optimizer based hyper heuristic approach Malaysia [26] Lecturer teaching scheduling that minimizes the difference of total teaching load using goal programming Indonesia [27] Multi-agent class timetabling for higher educational institutions using Prometheus platform Philippines [28] Particle swarm optimisation variants and its hybridisation ratios for generating cost-effective educational course timetables Thailand [29] Stemming the educational timetable problems Indonesia [18] University course timetabling model in joint courses program to minimize the number of unserved requests Indonesia [30] An effective hybrid local search approach for the post enrolment course timetabling problem Malaysia...…”

“…Table 4 shows the summary of the optimization approaches in this review. Answer set programming (ASP) [56] Hybrid Method Course Rescheduling Application System (CRAS) with implemented checking algorithm [11] Modified and hybridized bi-objective firefly algorithm (BOFA) with Pareto dominance approach [12] Multi-stage approach incorporating heuristics and grouping [15] Hybrid whale optimization algorithm that was a combination of the adapted whale optimization algorithm (WOA) and late acceptance hill climbing (LAHC) algorithm [16] Heuristic Ordering with a Perturbation technique (HO-P) [17] Lecturer-course assignment model developed by using integer linear programming and optimized by using cloud theory-based simulated annealing [18] Two stage heuristic algorithms with heuristics orderings, and hybrid of heuristic orderings and variable neighbourhood descent [20] Spotted Hyena Optimizer (SHO) and hybridization of SHO and Simulated Annealing (SA) [21] Iterated Local Search-Hill Climbing (ILS-HC) and Iterated Local Search-Simulated Annealing (ILS-SA) algorithms within hyper-heuristics [23] Class scheduling model using decorator and facade design patterns [24] Particle Swarm Optimizer based Hyper Heuristic (HH PSO)…”

Mathematical Modeling and Exact Optimizing of University Course Scheduling COnsidering Preferences of Professors

“…In order to develop this research further, it is suggested to consider the uncertainty in the important parameters of the mathematical model and implement robust optimization [33] to deal with such uncertainties. Moreover, due to the high complexity of the proposed mathematical model, it is suggested to apply efficient meta-heuristic algorithms such as a firefly algorithm (FFA) [34], whale optimization algorithm (WOA) [35], or pareto-based metaheuristics [36], to handle the complexities of the mathematical model.…”

Scouting Firefly Algorithm and its Performance on Global Optimization Problems

“…The limit parameter l is computed using: (6) where CS is the colony size and D is the dimension of the problem.…”

“…Since its inception almost 15 years ago, FA and its modified variants have demonstrated significant success in various fields of application. For example, in multilevel image segmentation [3], as a way to reduce the number of dimensions [4], optimizing convolutional neural networks [5], solving course timetabling problems [6], and dealing with complex engineering tasks [7], [8], among other things. Knowing that FA has a universal application makes it a fascinating subject to pursue.…”