Self-adaptive step firefly algorithm
type: Item
instance of: scholarly article
title: Self-adaptive step firefly algorithm
zbMATH Open document ID: 1397.90419
DOI: 10.1155/2013/832718
author: Shuhao Yu
author: Shoubao Su
author: Shan-Lin Yang
published in: Journal of Applied Mathematics
publication date: 2018-10-10T00:00:00Z
review text: Summary: In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly's historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6950896
Wikidata QID: Q59004727
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/832718
OpenAlex ID: W2001313240
DBLP publication ID: journals/jam/YuYS13
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description: scientific article; zbMATH DE number 6950896