Please use this identifier to cite or link to this item:
Title: JayaX: Jaya algorithm with xor operator for binary optimization
Authors: Aslan, Murat
Gündüz, Mesut
Kıran, Mustafa Servet
Keywords: Jaya
Binary Optimization
Logic Operator
Exclusive Or
Differential Evolution Algorithm
Particle Swarm Optimization
Bee Colony Algorithm
Artificial Algae Algorithm
Global Harmony Search
Design Optimization
Issue Date: 2019
Publisher: ELSEVIER
Abstract: Jaya is a population-based heuristic optimization algorithm proposed for solving constrained and unconstrained optimization problems. The peculiar distinct feature of Jaya from the other population-based algorithms is that it updates the positions of artificial agent in the population by considering the best and worst individuals. This is an important property for the algorithm to balance exploration and exploitation on the solution space. However, the basic Jaya cannot be applied to binary optimization problems because the solution space is discretely structured for this type of optimization problems and the decision variables of the binary optimization problems can be element of set [0,1]. In this study, we first focus on discretization of Jaya by using a logic operator, exclusive or - xor. The proposed idea is simple but effective because the solution update rule of Jaya is replaced with the xor operator, and when the obtained results are compared with the state-of-art algorithms, it is seen that the Jaya-based binary optimization algorithm, JayaX for short, produces better quality results for the binary optimization problems dealt with the study. The benchmark problems in this study are uncapacitated facility location problems and CEC2015 numeric functions, and the performance of the algorithms is compared on these problems. In order to improve the performance of the proposed algorithm, a local search module is also integrated with the JayaX. The obtained results show that the proposed algorithm is better than the compared algorithms in terms of solution quality and robustness. (C) 2019 Elsevier B.V. All rights reserved.
ISSN: 1568-4946
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 
  Until 2030-01-01
3.22 MBAdobe PDFView/Open    Request a copy
Show full item record

CORE Recommender


checked on Mar 18, 2023


checked on Jan 30, 2023

Page view(s)

checked on Mar 20, 2023

Google ScholarTM



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