Simulated annealing-based krill herd algorithm for global optimization
type: Item
instance of: scholarly article
title: Simulated annealing-based krill herd algorithm for global optimization
zbMATH Open document ID: 1291.90330
DOI: 10.1155/2013/213853
author: Gai-Ge Wang
author: Lihong Guo
author: A. H. Gandomi
author: Amir Hossein Alavi
author: Hong Duan
published in: Abstract and Applied Analysis
publication date: 2014-06-23T00:00:00Z
review text: Summary: Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH), for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH) method is proposed for optimization tasks. A new krill selecting (KS) operator is used to refine krill behavior when updating krill's position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA). In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 6306537
Wikidata QID: Q57691597
describes a project that uses: Krill herd
describes a project that uses: ABC
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/213853
OpenAlex ID: W2009607292
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description: scientific article; zbMATH DE number 6306537