Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1542
Title: Improving artificial algae algorithm performance by predicting candidate solution quality
Authors: Yibre, Abdulkerim Mohammed
Koçer, Barış
Keywords: Swarm Intelligence
Artificial Algae Algorithm
Naive Bayes
Candidate Solution Prediction
Particle Swarm Optimizer
Bee Colony Algorithm
Text Classification
Naive Bayes
Issue Date: 2020
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Abstract: The success of optimization algorithms is most of the time directly proportional to the number of fitness evaluations. However, not all fitness evaluations lead to successful fitness updates. Besides, the maximum number of fitness evaluations is limited and also balance of exploration and exploitation is still challenging. Best possible solution should be found in a reasonable time. Surely it can be said more fitness evaluation takes more time. Since methods are tested under fixed numbers of maximum fitness evaluation and the duration of each fitness evaluation of a problem may vary depending on the characteristic of the problem, finding best result with fewer fitness evaluations is challenging in optimization algorithms. For that reason in this study, we proposed a new method that predicts the quality of a candidate solution before evaluation of its fitness employing Gaussian-based Naive Bayes probabilistic model. If the candidate solution is predicted to generate good result then that solution is evaluated by the objective function. Otherwise new candidate solution is created as usual. The primary purpose of the proposed method is improving the performance of AAA and at the same time preventing unnecessary fitness evaluation. The proposed method is evaluated using standard benchmark functions and CEC'05 test suite. The obtained results suggests that the new method outperformed the basic AAA and other state-of-the-art meta-heuristic algorithms with fewer fitness evaluations. Thus, the new method can be extended to cost sensitive industrial problems. (c) 2020 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.eswa.2020.113298
https://hdl.handle.net/20.500.13091/1542
ISSN: 0957-4174
1873-6793
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Files in This Item:
File SizeFormat 
1-s2.0-S0957417420301238-main.pdf
  Until 2030-01-01
2.66 MBAdobe PDFView/Open    Request a copy
Show full item record



CORE Recommender

SCOPUSTM   
Citations

2
checked on Feb 24, 2024

WEB OF SCIENCETM
Citations

3
checked on Feb 24, 2024

Page view(s)

36
checked on Feb 26, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.