Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
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Abstract
Maximally Diverse Grouping Problem (MDGP) consists of grouping a set of M elements into G groups. The diversity of the elements in each group is maximized. The MDGP problem is one of the most complex computational problems today. It is the NP-Hard Problem. The Lofti-Cerveny-Weitz (LCW) method is a variation of the Lofti-Cerveny (LC) method. It explains that the search for element j is not limited to group g. LCW considers all groups when searching for j element except element i is located. Genetic algorithm is an intelligent optimization technique based on simulations of biological evolution. ZP Fan explains GA-based heuristic steps combined with Local Search for MDGP. The MDGP case of this study is at the ITERA dormitory. It divided into several attributes. They are regional origin, study programs, economics, religion and academic skills. Moreover, they were classified into groups and every group consisted of four students. Fitness value is calculated by adding up all the distance of students in each group. Student distance is obtained by using the Euclidian distance formula at the distance of the five student attributes. The result of this study showed that the fitness value using the Lorent-Cerveny-Weitz method was higher than the local search Genetic algorithm. It explained that male dormitory were 0.0066 and 0.0011% for female dormitory. The computation time of Lorent-Cerveny-Weitz Method was longer than the local search Genetic algorithm. It explained that male dormitory were 684 and 660% for female dormitory in longer duration.
How to cite this article:
RZ Abd. Aziz, Arief Apriandi and Riko Herwanto. The Comparison Between Lofti-Cerveny-Weitz Method and Local Search Genetic Algorithm
for Completion of Maximally Diverse Grouping Problem.
DOI: https://doi.org/10.36478/ajit.2020.289.293
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2020.289.293