2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA) 2019
DOI: 10.1109/iisa.2019.8900723
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Timetable Scheduling Using a Hybrid Particle Swarm Optimization with Local Search Approach

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Cited by 5 publications
(2 citation statements)
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“…[24], [25] presented an approach of coloring the edges of a two-part graph to solve the UCTTP problem, while [26] used graph coloring to schedule classes by assigning courses to a given number of time slots (colors). On the other hand, [27], [28], [29] proposed a genetic coloring approach to solve the UCTTP problem using a combination of graph coloring and genetic algorithms.…”
Section: Related Workmentioning
confidence: 99%
“…[24], [25] presented an approach of coloring the edges of a two-part graph to solve the UCTTP problem, while [26] used graph coloring to schedule classes by assigning courses to a given number of time slots (colors). On the other hand, [27], [28], [29] proposed a genetic coloring approach to solve the UCTTP problem using a combination of graph coloring and genetic algorithms.…”
Section: Related Workmentioning
confidence: 99%
“…Local search is one of the heuristic methods for combinatorial optimization problems in computer science and artificial intelligence. It has been demonstrated that local search is a simple but effective method for solving numerous computationally hard problems in computer science, mathematics, engineering, and bioinformatics, including the maximum satisfiability problem ( Gao, Li & Yin, 2017 ; Luo et al, 2017 , 2014 ), timetable scheduling ( Psarra & Apostolou, 2019 ), and clustering ( Tran et al, 2021 ; Levinkov, Kirillov & Andres, 2017 ). To solve complex optimization problems, local search algorithms with various search strategies have proven very effective in the literature.…”
Section: Proposed Local Search Algorithmmentioning
confidence: 99%