Business Intelligence for Paintball Tournament Matchmaking Using Particle Swarm Optimization
| Autor: | Khyrina Airin Fariza Abu Samah, Mohd Taufik Mishan, Nurhilyana Anuar, N.F. Baharin, Ahmad Firdaus Ahmad Fadzil |
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| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
education.field_of_study 021103 operations research Control and Optimization Operations research Computer Networks and Communications Event (computing) Computer science business.industry 05 social sciences Population 0211 other engineering and technologies Particle swarm optimization 02 engineering and technology ComputingMethodologies_ARTIFICIALINTELLIGENCE Swarm intelligence Hardware and Architecture 0502 economics and business Signal Processing Business intelligence Tournament Electrical and Electronic Engineering business education Information Systems |
| Zdroj: | Indonesian Journal of Electrical Engineering and Computer Science. 11:599 |
| ISSN: | 2502-4760 2502-4752 |
| DOI: | 10.11591/ijeecs.v11.i2.pp599-606 |
| Popis: | Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population. |
| Databáze: | OpenAIRE |
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