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A novel modified bat algorithm hybridizing by differential evolution algorithm

Expert Systems with Applications · Mart 2020

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YÖKSİS Kayıtları
A novel modified bat algorithm hybridizing by differential evolution algorithm
Expert Systems with Applications · 2020 SCI-Expanded
Dr. Öğr. Üyesi GÜLNUR YILDIZDAN →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 20 kaydı bulundu.

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Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS
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Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine
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An Adaptive Network Based Fuzzy Inference System ANFIS for the prediction of stock market return The case of the Istanbul Stock Exchange
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Predicting direction of stock price index movement using artificial neural networks and support vector machines The sample of the Istanbul Stock Exchange
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Makale Bilgileri

Dergi Expert Systems with Applications
ISSN09574174
Yayın TarihiMart 2020
Cilt / Sayfa141
DOI10.1016/j.eswa.2019.112949
Scopus ID2-s2.0-85072528270
Özet The bat algorithm (BA) is one of the metaheuristic algorithms that are used to solve optimization problems. The differential evolution (DE) algorithm is also applied to optimization problems and has successful exploitation ability. In this study, an advanced modified BA (MBA) algorithm was initially proposed by making some modifications to improve the exploration and exploitation abilities of the BA. A hybrid system (MBADE), involving the use of the MBA in conjunction with the DE, was then suggested in order to further improve the exploitation potential and provide superior performance in various test problem clusters. The proposed hybrid system uses a common population, and the algorithm to be applied to the individual is selected on the basis of a probability value, which is calculated in accordance with the performance of the algorithms; thus, the probability of applying a successful algorithm is increased. The performance of the proposed method was tested on functions that have frequently been studied, such as classical benchmark functions, small-scale CEC 2005 benchmark functions, large-scale CEC 2010 benchmark functions, and CEC 2011 real-world problems. The obtained results were compared with the results obtained from the standard BA and other findings in the literature and interpreted by means of statistical tests. The developed hybrid system showed superior performance to the standard BA in all test problem sets and produced more acceptable results when compared to the published data for the existing algorithms. In addition, the contribution of the MBA and DE algorithms to the hybrid system was examined.

Yazarlar (2)

1
Gülnur Yildizdan
2
Omer Kaan Baykan

Anahtar Kelimeler

Bat algorithm Continuous optimization Differential evolution algorithm Heuristic algorithms Large-scale optimization

Kurumlar

Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Expert Systems with Applications
Q1
SJR Skoru1,854
H-Index290
YayıncıElsevier Ltd
ÜlkeUnited Kingdom
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Engineering (miscellaneous) (Q1)
Dergi sayfasına git

Metrikler

92
Atıf
2
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

GY
GÜLNUR YILDIZDAN
Dr. Öğr. Üyesi

Hızlı Erişim

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