An Integrated Gapso Approach for Solving Problem of an Examination Timetabiking System

摘要

Examination timetabling is a discrete, multi-objective and combinatorial optimization problem which tends to be solved with a cooperation of stochastic search approaches such as particle swarm optimization (PSO) and the Genetic Algorithm (GA). PSO is a well-known one of the popular swarm intelligent algorithm. used successfully for several complicated combinatorial optimization problems. Throughout the years, educational institutions have been confronted by the problems related to changing the time to their schedule. In order to compete for the growing number of students, educators must offer three or four final examinations every year. Furthermore, to approach this problem, in this study going to use the enhanced hybrid method for resolving the issue. The proposed study, a GA and PSO algorithms were utilized together to find a solution to the exam scheduling issue. The results of the study show that our approach exceeds the GA and PSO approaches by achieving 90% best of mean. The effectiveness of the approach can be hurt by any change to its parameters.