State Flipping Based Hyper-Heuristic for Hybridization of Nature Inspired Algorithms

The paper presents a novel hyper-heuristic strategy for hybridization of nature inspired algorithms. The strategy is based on switching the state of agents using a logistic probability function, which depends upon the fitness rank of an agent. A case study using two nature inspired algorithms (Artif...

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Published inArtificial Intelligence and Soft Computing Vol. 10245; pp. 337 - 346
Main Authors Damaševičius, Robertas, Woźniak, Marcin
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2017
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Metaheuristic
Nature inspired algorithms
Optimization
Online AccessGet full text
ISBN9783319590622
3319590626
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-59063-9_30

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Abstract The paper presents a novel hyper-heuristic strategy for hybridization of nature inspired algorithms. The strategy is based on switching the state of agents using a logistic probability function, which depends upon the fitness rank of an agent. A case study using two nature inspired algorithms (Artificial Bee Colony (ABC) and Krill Herding (KH)) and eight optimization problems (Ackley Function, Bukin Function N.6, Griewank Function, Holder Table Function, Levy Function, Schaffer Function N.2, Schwefel Function, Shubert Function) is presented. The results show a superiority of the proposed hyper-heuristic (mean end-rank for hybrid algorithm is 1.435 vs. 2.157 for KH and 2.408 for ABC).
AbstractList The paper presents a novel hyper-heuristic strategy for hybridization of nature inspired algorithms. The strategy is based on switching the state of agents using a logistic probability function, which depends upon the fitness rank of an agent. A case study using two nature inspired algorithms (Artificial Bee Colony (ABC) and Krill Herding (KH)) and eight optimization problems (Ackley Function, Bukin Function N.6, Griewank Function, Holder Table Function, Levy Function, Schaffer Function N.2, Schwefel Function, Shubert Function) is presented. The results show a superiority of the proposed hyper-heuristic (mean end-rank for hybrid algorithm is 1.435 vs. 2.157 for KH and 2.408 for ABC).
Author Damaševičius, Robertas
Woźniak, Marcin
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Editor Scherer, Rafał
Zadeh, Lotfi A
Tadeusiewicz, Ryszard
Zurada, Jacek M
Rutkowski, Leszek
Korytkowski, Marcin
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Snippet The paper presents a novel hyper-heuristic strategy for hybridization of nature inspired algorithms. The strategy is based on switching the state of agents...
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StartPage 337
SubjectTerms Metaheuristic
Nature inspired algorithms
Optimization
Title State Flipping Based Hyper-Heuristic for Hybridization of Nature Inspired Algorithms
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