IEICE TRANSACTIONS on Information
Novel Metaheuristic: Spy Algorithm
Summary :
We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. The design of our spy algorithm ensures the benefit of exploration and exploitation as well as cooperative and non-cooperative searches in each iteration. We compared the spy algorithm with genetic algorithm, improved harmony search, and particle swarm optimization on a set of non-convex functions that focus on accuracy, the ability of detecting many global optimum points, and computation time. From statistical analysis results, the spy algorithm outperformed the other algorithms. The spy algorithm had the best accuracy and detected more global optimum points within less computation time, indicating that our spy algorithm is more robust and faster then these other algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.2 pp.309-319
- Publication Date
- 2022/02/01
- Publicized
- 2021/11/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDP7092
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Dhidhi PAMBUDI
Yamaguchi University,Sebelas Maret University
Masaki KAWAMURA
Yamaguchi University
Keyword
Latest Issue
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Dhidhi PAMBUDI, Masaki KAWAMURA, "Novel Metaheuristic: Spy Algorithm" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 2, pp. 309-319, February 2022, doi: 10.1587/transinf.2021EDP7092.
Abstract: We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. The design of our spy algorithm ensures the benefit of exploration and exploitation as well as cooperative and non-cooperative searches in each iteration. We compared the spy algorithm with genetic algorithm, improved harmony search, and particle swarm optimization on a set of non-convex functions that focus on accuracy, the ability of detecting many global optimum points, and computation time. From statistical analysis results, the spy algorithm outperformed the other algorithms. The spy algorithm had the best accuracy and detected more global optimum points within less computation time, indicating that our spy algorithm is more robust and faster then these other algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7092/_p
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@ARTICLE{e105-d_2_309,
author={Dhidhi PAMBUDI, Masaki KAWAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Novel Metaheuristic: Spy Algorithm},
year={2022},
volume={E105-D},
number={2},
pages={309-319},
abstract={We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. The design of our spy algorithm ensures the benefit of exploration and exploitation as well as cooperative and non-cooperative searches in each iteration. We compared the spy algorithm with genetic algorithm, improved harmony search, and particle swarm optimization on a set of non-convex functions that focus on accuracy, the ability of detecting many global optimum points, and computation time. From statistical analysis results, the spy algorithm outperformed the other algorithms. The spy algorithm had the best accuracy and detected more global optimum points within less computation time, indicating that our spy algorithm is more robust and faster then these other algorithms.},
keywords={},
doi={10.1587/transinf.2021EDP7092},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Novel Metaheuristic: Spy Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 309
EP - 319
AU - Dhidhi PAMBUDI
AU - Masaki KAWAMURA
PY - 2022
DO - 10.1587/transinf.2021EDP7092
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2022
AB - We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. The design of our spy algorithm ensures the benefit of exploration and exploitation as well as cooperative and non-cooperative searches in each iteration. We compared the spy algorithm with genetic algorithm, improved harmony search, and particle swarm optimization on a set of non-convex functions that focus on accuracy, the ability of detecting many global optimum points, and computation time. From statistical analysis results, the spy algorithm outperformed the other algorithms. The spy algorithm had the best accuracy and detected more global optimum points within less computation time, indicating that our spy algorithm is more robust and faster then these other algorithms.
ER -
English
