Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem

Valdi Rachman, M. Anwar Ma'sum

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

1 Citation (Scopus)

Abstract

Software Project Scheduling Problem (SPSP) is one of Project Scheduling Problem which is classified as NP-Hard problem. In 2014, variation of Ant Colony Optimization (ACO) algorithms was successfully developed. The algorithm is Max-Min Ant System (MMAS) that proposed to solve SPSP. In 2012, there is variation of ACO named Ant Colony Extended (ACE) developed for Travelling Salesman Problem and it shows better performance than well-known ACO algorithms: MMAS and Ant Colony System (ACS). However, there is no research about ACE's performance in SPSP where MMAS is successfully applied. In this paper, ACE and MMAS algorithm were compared in SPSP. The experiment result shows that ACE has better performance than MMAS for SPSP. The performance is indicated by fitness value of the algorithms.

Original languageEnglish
Title of host publicationProceedings - WBIS 2017
Subtitle of host publication2017 International Workshop on Big Data and Information Security
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages85-91
Number of pages7
ISBN (Electronic)9781538620380
DOIs
Publication statusPublished - 29 Jan 2018
Event2017 International Workshop on Big Data and Information Security, WBIS 2017 - Jakarta, Indonesia
Duration: 23 Sep 201724 Sep 2017

Publication series

NameProceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
Volume2018-January

Conference

Conference2017 International Workshop on Big Data and Information Security, WBIS 2017
Country/TerritoryIndonesia
CityJakarta
Period23/09/1724/09/17

Keywords

  • Ant Colony Optimization
  • Project management
  • Software Project Scheduling Problem

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