Evaluation of Hyper-Heuristic Method Using Random-Hill Climbing Algorithm in the Examination Timetabling Problem

Examination timetabling is included in the category of Nondeterministic Polynomial-Hard (NP-Hard) problems, namely problems that cannot be solved by conventional methods in finding optimal solutions. One solution to this problem is to use the Simple Random - Hill Climbing - Hyper Heuristic (SR-HC-HH...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-07, Vol.1569 (2), p.22101
Main Authors: Hermansyah, Deny, Muklason, Ahmad
Format: Article
Language:English
Subjects:
Algorithms
Data retrieval
Data structures
Examination Timetabling Problem
Heuristic
Heuristic methods
Hill Climbing
Hyper-Heuristic
ITC 2007
Low level
Optimization
Parameters
Performance evaluation
Physics
Polynomials
Simple Random
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Summary:Examination timetabling is included in the category of Nondeterministic Polynomial-Hard (NP-Hard) problems, namely problems that cannot be solved by conventional methods in finding optimal solutions. One solution to this problem is to use the Simple Random - Hill Climbing - Hyper Heuristic (SR-HC-HH) approach. But this approach still cannot produce an optimum solution. The researcher presents a critical analysis of the performance evaluation of the solution method used. The stages of this research include: (1) problem identification; (2) literature study; (3) data retrieval and understanding; (4) translation of mathematical models into data structures; (5) evaluation of the SR-HC-HH algorithm; (6) trial implementation; (7) algorithmic experiment parameters; and (8) analysis of results and conclusions. SR-HC-HH algorithm used in Hyper-Heuristics-based applications is able to solve timetabling problems in the Examination Timetabling Problem domain in the ITC-2007 dataset, but it is still not optimal. Parameters that can be changed in this study are the number of iterations (time limit) and algorithms for selection of Low Level Heuristics. Changing parameters in the trial scenario can also affect the results of a more optimum solution. The parameters to be explored in this study include LLH selection strategies and move acceptance in hyper-heuristics.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1569/2/022101