@inproceedings{60b7d566f3214b48b4d4e8d3338bcd93,
title = "Discovering optimized process model using rule discovery hybrid particle swarm optimization",
abstract = "This paper presents a bio-inspired hybrid method which concentrate on the optimal or a near-optimal business process model from an event log. The discovery of Hybrid Particle Swarm Optimization (Hybrid PSO) algorithm comes from the combination of Particle Swarm Optimization (PSO) algorithm and Simulated Annealing (SA) method. This paper presents a method which combines Rule discovery task and Hybrid PSO. The proposed method can discover not only classification rules that produce the most optimal business process model from event logs, but also can optimize the quality of process model. To be formulated into an optimization problem, we use rule discovery task to get the high accuracy, comprehensibility and generalization performance. After we get the results from rule discovery task, we use Hybrid PSO to resolve the problem. In this proposed method, we use continuous data as data set and fitness function as evaluation criteria of quality of discovered business process model. As final results, we prove that the proposed method has the best results in terms of average fitness and number of iterations, compared with classical PSO algorithm and original hybrid PSO algorithm.",
keywords = "average fitness, business process, Hybrid PSO, optimization, Particle Swarm Optimization, process mining, rule discovery, Simulated Annealing",
author = "Effendi, {Yutika Amelia} and Riyanarto Sarno",
note = "Funding Information: ACKNOWLEDGMENT Authors would like to thank the Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember for supporting this research. Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd International Conference on Science in Information Technology, ICSITech 2017 ; Conference date: 25-10-2017 Through 26-10-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/ICSITech.2017.8257092",
language = "English",
series = "Proceeding - 2017 3rd International Conference on Science in Information Technology: Theory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "97--103",
editor = "Riza, {Lala Septem} and Andri Pranolo and Wibawa, {Aji Prasetyo} and Enjun Junaeti and Yaya Wihardi and Hashim, {Ummi Raba'ah} and Shi-Jinn Horng and Rafal Drezewski and Lim, {Heui Seok} and Goutam Chakraborty and Leonel Hernandez and Shah Nazir",
booktitle = "Proceeding - 2017 3rd International Conference on Science in Information Technology",
address = "United States",
}