A Step Counting Hill Climbing Algorithm applied to University Examination Timetabling

Abstract

This paper presents a new single-parameter local search heuristic named step counting hill climbing algorithm (SCHC). It is a very simple method in which the current cost serves as an acceptance bound for a number of consecutive steps. This is the only parameter in the method that should be set up by the user. Furthermore, the counting of steps can be organised in different ways; therefore, the proposed method can generate a large number of variants and also extensions. In this paper, we investigate the behaviour of the three basic variants of SCHC on the university exam timetabling problem. Our experiments demonstrate that the proposed method shares the main properties with the late acceptance hill climbing method, namely its convergence time is proportional to the value of its parameter and a non-linear rescaling of a problem does not affect its search performance. However, our new method has two additional advantages: a more flexible acceptance condition and better overall performance. In this study, we compare the new method with late acceptance hill climbing, simulated annealing and great deluge algorithm. The SCHC has shown the strongest performance on the most of our benchmark problems used.

Comparative Analysis of Different Data Representations for the Task of Chemical Compound Extraction

“…It is important to note that the search algorithm used in our study is Hill Climbing. Hill Climbing (HC) is a heuristic search algorithm that seeks to find nearly optimized solutions [22]. HC is a local search algorithm that has been used on hard optimization problems.…”

Modelling the Universiti Malaysia Pahang Examination Timetabling Problem

“…The objectives of the ITC2007 examination dataset are to minimise the back-to-back exams on the same day, timeslot and room usage, mixed examinations length in a timeslot and assigning as early as possible those larger examinations during the period of exam. Many researchers have investigated ITC2007 examination dataset including Gogos et al, 2012;Bykov and Petrovic, 2016;Alzaqebah and Abdullah, 2014;Battistutta et al, 2015;Anwar et al, 2013. An uncapacitated dataset from UiTM Malaysia was introduced by Kendall and Hussin (2004). UiTM dataset consists of 2063 examinations, 84675 registered students, 357761 student enrolments and 40 timeslots.…”

AI 2018: Advances in Artificial Intelligence

“…In this section we evaluate the performance of the proposed DLAS method, the most recent version of LAHC (as described in Sec. 2), and the recently proposed Step Counting Hill Climbing (SCHC) [9]. All experiments were ran on the same computing cluster with a 500 Mb memory limit.…”