An accurate partially attracted firefly algorithm

The firefly algorithm (FA) is a new and powerful algorithm for optimization. However, it has the disadvantages of high computational complexity and low convergence accuracy, especially when solving complex problems. In this paper, an accurate partially attracted firefly algorithm (PaFA) is proposed...

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Main Authors: Zhou, Lingyun, Ding, Lixin, Ma, Maode, Tang, Wan
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/139039
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1390392020-05-15T02:30:55Z An accurate partially attracted firefly algorithm Zhou, Lingyun Ding, Lixin Ma, Maode Tang, Wan School of Electrical and Electronic Engineering Engineering::Computer science and engineering Firefly Algorithm Partial Attraction Model The firefly algorithm (FA) is a new and powerful algorithm for optimization. However, it has the disadvantages of high computational complexity and low convergence accuracy, especially when solving complex problems. In this paper, an accurate partially attracted firefly algorithm (PaFA) is proposed by adopting a partial attraction model and a fast attractiveness calculation strategy. The partial attraction model can preserve swarm diversity and make full use of individual information. The fast attractiveness calculation strategy ensures information sharing among the individuals and it also improves the convergence accuracy. The experimental results demonstrate the good performance of PaFA in terms of the solution accuracy compared with two state-of-the-art FA variants and two other bio-inspired algorithms. 2020-05-15T02:30:55Z 2020-05-15T02:30:55Z 2018 Journal Article Zhou, L., Ding, L., Ma, M., & Tang, W. (2019). An accurate partially attracted firefly algorithm. Computing, 101(5), 477-493. doi:10.1007/s00607-018-0645-2 0010-485X https://hdl.handle.net/10356/139039 10.1007/s00607-018-0645-2 2-s2.0-85050256682 5 101 477 493 en Computing © 2018 Springer-Verlag GmbH Austria, part of Springer Nature. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Firefly Algorithm
Partial Attraction Model
spellingShingle Engineering::Computer science and engineering
Firefly Algorithm
Partial Attraction Model
Zhou, Lingyun
Ding, Lixin
Ma, Maode
Tang, Wan
An accurate partially attracted firefly algorithm
description The firefly algorithm (FA) is a new and powerful algorithm for optimization. However, it has the disadvantages of high computational complexity and low convergence accuracy, especially when solving complex problems. In this paper, an accurate partially attracted firefly algorithm (PaFA) is proposed by adopting a partial attraction model and a fast attractiveness calculation strategy. The partial attraction model can preserve swarm diversity and make full use of individual information. The fast attractiveness calculation strategy ensures information sharing among the individuals and it also improves the convergence accuracy. The experimental results demonstrate the good performance of PaFA in terms of the solution accuracy compared with two state-of-the-art FA variants and two other bio-inspired algorithms.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhou, Lingyun
Ding, Lixin
Ma, Maode
Tang, Wan
format Article
author Zhou, Lingyun
Ding, Lixin
Ma, Maode
Tang, Wan
author_sort Zhou, Lingyun
title An accurate partially attracted firefly algorithm
title_short An accurate partially attracted firefly algorithm
title_full An accurate partially attracted firefly algorithm
title_fullStr An accurate partially attracted firefly algorithm
title_full_unstemmed An accurate partially attracted firefly algorithm
title_sort accurate partially attracted firefly algorithm
publishDate 2020
url https://hdl.handle.net/10356/139039
_version_ 1681056452611932160