Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Moth-Flame Optimization and Ant Nesting Algorithm: A Systematic Evaluation

Authors
Hanan K. AbdulKarim1, *, Tarik A. Rashid2
1Software Engineering Department, College of Engineering, Salahaddin University - Erbil, Erbil, Iraq
2Computer Science and Engineering Department, University of Kurdistan Hewler, Erbil, Iraq
*Corresponding author. Email: hanan.abdulkarim@su.edu.krd
Corresponding Author
Hanan K. AbdulKarim
Available Online 30 January 2023.
DOI
10.2991/978-94-6463-110-4_11How to use a DOI?
Keywords
Metaheuristics; Moth-Flame Optimization; Ant Nesting Algorithm
Abstract

In this paper, some swarm metaheuristic algorithms are analyzed to show the performance of algorithms. Swarm Intelligence algorithms are more trapping in local optima because of low exploration. The Moth-Flame Optimization algorithm is a widely applied metaheuristic algorithm. Nevertheless, the Ant Nesting Algorithm is another recent powerful algorithm. Both algorithms are theoretically and practically studied and applied to a simple optimization problem with a simple objective function. All steps of the algorithms are implemented and discussed providing results to show and compare the exploration and exploitation performance between both algorithms. A simple comparison between both algorithms is conducted using the same sample of data, and it is concluded that convergence within cycles of implementation shows that Ant Nesting Algorithm is fast converged but it might get stuck in local optima because of low exploration. Additionally, the merits of the Ant Nesting Algorithm show the algorithm will stuck in local optima when solving highly complex problems or if the initial population can't be explored efficiently. Moth-Flame Optimization also may have exploration problems when the size of flames during cycles is decreased.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
Series
Advances in Computer Science Research
Publication Date
30 January 2023
ISBN
10.2991/978-94-6463-110-4_11
ISSN
2352-538X
DOI
10.2991/978-94-6463-110-4_11How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Hanan K. AbdulKarim
AU  - Tarik A. Rashid
PY  - 2023
DA  - 2023/01/30
TI  - Moth-Flame Optimization and Ant Nesting Algorithm: A Systematic Evaluation
BT  - Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
PB  - Atlantis Press
SP  - 139
EP  - 152
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-110-4_11
DO  - 10.2991/978-94-6463-110-4_11
ID  - AbdulKarim2023
ER  -