Discovering optimized process model using rule discovery hybrid particle swarm optimization

Yutika Amelia Effendi, Riyanarto Sarno

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceeding - 2017 3rd International Conference on Science in Information Technology
Subtitle of host publicationTheory and Application of IT for Education, Industry and Society in Big Data Era, ICSITech 2017
EditorsLala Septem Riza, Andri Pranolo, Aji Prasetyo Wibawa, Enjun Junaeti, Yaya Wihardi, Ummi Raba'ah Hashim, Shi-Jinn Horng, Rafal Drezewski, Heui Seok Lim, Goutam Chakraborty, Leonel Hernandez, Shah Nazir
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages97-103
Number of pages7
ISBN (Electronic)9781509058662
DOIs
Publication statusPublished - 1 Jul 2017
Externally publishedYes
Event3rd International Conference on Science in Information Technology, ICSITech 2017 - Bandung, Indonesia
Duration: 25 Oct 201726 Oct 2017

Publication series

NameProceeding - 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
Volume2018-January

Conference

Conference3rd International Conference on Science in Information Technology, ICSITech 2017
Country/TerritoryIndonesia
CityBandung
Period25/10/1726/10/17

Keywords

  • Hybrid PSO
  • Particle Swarm Optimization
  • Simulated Annealing
  • average fitness
  • business process
  • optimization
  • process mining
  • rule discovery

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