Moth Search Algorithm for Bound Constrained Optimization Problems

Raka Jovanovic; Eva Tuba; Romana Capor-Hrosik; Adis Alihodzic; Marko Beko

Moth Search Algorithm for Bound Constrained Optimization Problems

Raka Jovanovic, Eva Tuba, Romana Capor-Hrosik, Adis Alihodzic, Marko Beko

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

Abstract

Numerous real life problems represent hard optimization problems that cannot be solved by deterministic algorithms. In the past decades, various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms, especially swarm intelligence algorithms. Moth search optimization algorithm (MSO) is on of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested MSO algorithm on 15 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that MSO has good characteristics and it outperformed other approach from literature.

Original language	English
Title of host publication	Proceeding of the 5th International Conference MDIS
Subtitle of host publication	Modelling and Development of Intelligent Systems
Publication status	Published - Jun 2017
Externally published	Yes

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Jovanovic, R., Tuba, E., Capor-Hrosik, R., Alihodzic, A., & Beko, M. (2017). Moth Search Algorithm for Bound Constrained Optimization Problems. In Proceeding of the 5th International Conference MDIS: Modelling and Development of Intelligent Systems https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wpDJ5t4AAAAJ&cstart=100&pagesize=100&citation_for_view=wpDJ5t4AAAAJ:D03iK_w7-QYC

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title = "Moth Search Algorithm for Bound Constrained Optimization Problems",

abstract = "Numerous real life problems represent hard optimization problems that cannot be solved by deterministic algorithms. In the past decades, various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms, especially swarm intelligence algorithms. Moth search optimization algorithm (MSO) is on of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested MSO algorithm on 15 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that MSO has good characteristics and it outperformed other approach from literature.",

author = "Raka Jovanovic and Eva Tuba and Romana Capor-Hrosik and Adis Alihodzic and Marko Beko",

year = "2017",

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language = "English",

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Jovanovic, R, Tuba, E, Capor-Hrosik, R, Alihodzic, A & Beko, M 2017, Moth Search Algorithm for Bound Constrained Optimization Problems. in Proceeding of the 5th International Conference MDIS: Modelling and Development of Intelligent Systems. <https://scholar.google.com/citations?view_op=view_citation&hl=en&user=wpDJ5t4AAAAJ&cstart=100&pagesize=100&citation_for_view=wpDJ5t4AAAAJ:D03iK_w7-QYC>

TY - GEN

T1 - Moth Search Algorithm for Bound Constrained Optimization Problems

AU - Jovanovic, Raka

AU - Tuba, Eva

AU - Capor-Hrosik, Romana

AU - Alihodzic, Adis

AU - Beko, Marko

PY - 2017/6

Y1 - 2017/6

N2 - Numerous real life problems represent hard optimization problems that cannot be solved by deterministic algorithms. In the past decades, various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms, especially swarm intelligence algorithms. Moth search optimization algorithm (MSO) is on of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested MSO algorithm on 15 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that MSO has good characteristics and it outperformed other approach from literature.

AB - Numerous real life problems represent hard optimization problems that cannot be solved by deterministic algorithms. In the past decades, various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms, especially swarm intelligence algorithms. Moth search optimization algorithm (MSO) is on of the recent swarm intelligence algorithm that has not been thoroughly researched. In this paper we tested MSO algorithm on 15 standard benchmark functions and compared results with particle swarm optimization algorithm. Comparison show that MSO has good characteristics and it outperformed other approach from literature.

M3 - Conference contribution

BT - Proceeding of the 5th International Conference MDIS

ER -

Moth Search Algorithm for Bound Constrained Optimization Problems

Abstract

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