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A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search

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Abstract

Scheduling is one of the problems that has attracted the attention of many researchers over the years. The University Course Timetabling Problem (UCTP) is a highly constrained real-world combinatorial optimization task. Designing course timetables for academic institutions has always been challenging, because it is a non-deterministic polynomial-time hardness (NP-hard) problem. This problem attempts to assign specific timeslots and rooms to the events considering a number of hard and soft constraints. All hard constraints must be satisfied to achieve a feasible solution, whereas satisfying all soft constraints is not necessary. Although the quality of the solution is directly related to the number of soft constraints that are satisfied. One of the recent innovative methodologies for solving UCTP is the hybrid algorithm, which attempts to automate the timetabling design process so that it would be able to work with different instances of problem domains. In this paper, we present a hybrid method based on the Improved Parallel Genetic Algorithm and Local Search (IPGALS) to solve the course timetabling problem. The Local Search (LS) approach is used to strengthen the Genetic Algorithm (GA). The IPGALS has applied a representation of the timetable, which ensure the hard constraints would never be violated. Hard constraints are measured by Distance to Feasibility (DF) criterion. In fact, applying the DF criterion leads to achieving feasible solutions and promotes the performance of our algorithm. Due to the wide range of problem constraints, the proposed algorithm is performed in parallel to improve the GA searching process. The IPGALS algorithm is tested over BenPaechter and ITC-2007 standard benchmarks and compared with the state-of-the-art techniques in this literature. The experimental results confirm the effectiveness and the superiority of the proposed algorithm compared to other prominent methods for solving UCTP.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Rahjuyan Danesh Borazjan, Bushehr, Iran

    Amin Rezaeipanah

  2. Department of Computer Engineering, Bushehr Branch, Islamic Azad University, Bushehr, Iran

    Samaneh Sechin Matoori

  3. Department of Computer Engineering, Persian Gulf University, Bushehr, Iran

    Gholamreza Ahmadi

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Appendix

Appendix

Full names of applied algorithms (Ai) in Tables 11, 12 and 13. A1: (RIICN: Randomize Iterative Improvement with Composite Nears [52]), A2: (GBHH: Graph Based Hyper Heuristic [53]), A3: (HEA: Hybrid Evolutionary Algorithm [54]), A4: (NLGD: Non-Linear Great Deluge [55]), A5: (MA: Memetic Algorithm [56]), A6: (VNS: Variable Neighborhood Search [52]), A7: (THH: Taboo based Hyper Heuristics [44]), A8: (LS: Local Search [57]), A9: (EA: Evolutionary Algorithm [58]), A10: (ACO: Ant Colony Optimization [59]), A11: (FA: Fuzzy Approach [60]), A12: (EGSGA: Extended Guided Search with Genetic Algorithm [20]), A13: (GA w LS: Genetic Algorithm with Local Search [61]), A14: (GA: Genetic Algorithm [25]), A15: (HGA: Hybrid Genetic Algorithm [62]), A16: (RIIA: Randomised Iterative Improvement Algorithm [44]), A17: (HEA: Hybrid Evolutionary Approach [63]), A18: (GHH: Graph-based Hyper Heuristic [53]), A19: (VNS: Variable Neighborhood Search [64]), A20: (TSH: Tabu-Search Hyperheuristic [65]), A21: (FMH: Fuzzy Multiple Heuristic [66]), A22: (GSGA: Guided Search Genetic Algorithm [48]), A23: (HA: Hybrid Algorithm [67]), A24: (HGATS: Hybrid Genetic Algorithm and Tabu Search [68]), A25: (MMH: Mixed Meta-Heuristic [69]), A26: (HA: Hybrid Algorithm [24]), A27: (ACO w ILS: Ant Colony Optimization with Iterative Local Search [59]), A28: (LS: Local Search [26]), A29: (HHADL: Hyper-Heuristic with Add Delete Lists [43]), A30: (GAGLS: Genetic Algorithms with Guided and Local Search [20]), A31: (TDMH: Time-Dependent Meta-Heuristic [70]), A32: (SA: Simulated Annealing [71]), A33: (HH: Hyper-Heuristics [72]), A34: (TS-ILS: Tabu Search And Iterated Local Search [73]), A36: (CB-CTT: Curriculum-Based Course TimeTabling [74]), A37: (RPNS: Random Partial Neighborhood Search [75]), A38: (SAIRL: Simulated Annealing with Improved Reheating and Learning [32]), A39: (IPGALS: Improved Parallel Genetic Algorithm and Local Search (Proposed Algorithm)).

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Rezaeipanah, A., Matoori, S.S. & Ahmadi, G. A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search. Appl Intell 51, 467–492 (2021). https://doi.org/10.1007/s10489-020-01833-x

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