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

Valdi Rachman; M. Anwar Ma'sum

doi:10.1109/IWBIS.2017.8275107

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

Valdi Rachman, M. Anwar Ma'sum

Faculty of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (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 language	English
Title of host publication	Proceedings - WBIS 2017
Subtitle of host publication	2017 International Workshop on Big Data and Information Security
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	85-91
Number of pages	7
ISBN (Electronic)	9781538620380
DOIs	https://doi.org/10.1109/IWBIS.2017.8275107
Publication status	Published - 29 Jan 2018
Event	2017 International Workshop on Big Data and Information Security, WBIS 2017 - Jakarta, Indonesia Duration: 23 Sept 2017 → 24 Sept 2017

Publication series

Name	Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
Volume	2018-January

Conference

Conference	2017 International Workshop on Big Data and Information Security, WBIS 2017
Country/Territory	Indonesia
City	Jakarta
Period	23/09/17 → 24/09/17

Keywords

Ant Colony Optimization
Project management
Software Project Scheduling Problem

Access to Document

10.1109/IWBIS.2017.8275107

Cite this

Rachman, V., & Ma'sum, M. A. (2018). Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem. In Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security (pp. 85-91). (Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWBIS.2017.8275107

Rachman, Valdi ; Ma'sum, M. Anwar. / Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem. Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 85-91 (Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security).

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

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.",

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author = "Valdi Rachman and Ma'sum, {M. Anwar}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Workshop on Big Data and Information Security, WBIS 2017 ; Conference date: 23-09-2017 Through 24-09-2017",

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doi = "10.1109/IWBIS.2017.8275107",

language = "English",

series = "Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security",

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Rachman, V & Ma'sum, MA 2018, Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem. in Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security. Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 85-91, 2017 International Workshop on Big Data and Information Security, WBIS 2017, Jakarta, Indonesia, 23/09/17. https://doi.org/10.1109/IWBIS.2017.8275107

Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem. / Rachman, Valdi; Ma'sum, M. Anwar.
Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security. Institute of Electrical and Electronics Engineers Inc., 2018. p. 85-91 (Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem

AU - Rachman, Valdi

AU - Ma'sum, M. Anwar

PY - 2018/1/29

Y1 - 2018/1/29

N2 - 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.

AB - 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.

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Rachman V, Ma'sum MA. Comparative analysis of ant colony extended and mix-min ant system in software project scheduling problem. In Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security. Institute of Electrical and Electronics Engineers Inc. 2018. p. 85-91. (Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security). doi: 10.1109/IWBIS.2017.8275107

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

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