Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q3
Comparison of NSGA-II and MODE performances by using MCDM methods for multi-response experimental data
Sigma Journal of Engineering and Natural Sciences · Şubat 2025
YÖKSİS Kayıtları
Comparison of NSGA-II and MODE performances by using MCDM methods for multi-response experimental data
Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi · 2025 ESCI
Prof. Dr. NİMET YAPICI PEHLİVAN →
YÖKSİS Kayıtları — ISSN Eşleşmesi
ESTIMATION OF PARAMETER FOR INVERSE RAYLEIGH DISTRIBUTION UNDER TYPE-I HYBRID CENSORED SAMPLES
2020 ISSN: 1304-7191 ESCI
Doç. Dr. YUNUS AKDOĞAN →
Tribological behavior of epoxy nanocomposites under corrosive environment: effect of high-performance boron nitride nanoplatelet
2022 ISSN: 1304-7191 Emerging Sources Citation Index (ESCI), EBSCO Host Online Research Databases, Directory of Open Access Journals (DOAJ)
Doç. Dr. HASAN ULUS →
A new distribution with four parameters: Properties and applications
2023 ISSN: 1304-7191 ESCI
Doç. Dr. KADİR KARAKAYA →
A new distribution with four parameters: Properties and applications
2023 ISSN: 1304-7191 ESCI
Prof. Dr. COŞKUN KUŞ →
A new distribution with four parameters: Properties and applications
2023 ISSN: 1304-7191 ESCI
Prof. Dr. İSMAİL KINACI →
Makale Bilgileri
ISSN13047191
Yayın TarihiŞubat 2025
Cilt / Sayfa43 · 133-147
Scopus ID2-s2.0-86000178368
Erişim🔓 Açık Erişim
Özet
Multi-response experimental data, composed with more than one response variable, can be examined in three stages: modeling, optimization and decision making. In this study, these three stages were considered sequentially. Model parameters were estimated through Seemingly Unrelated Regression (SUR) method due to linear correlation between responses during the modeling stage. In the optimization stage, simultaneous optimization of predicted multiple responses were considered as a multi-objective optimization (MOO) problem. For this purpose, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi Objective Differential Evolution (MODE), were applied to obtain Pareto solution sets. In the decision making stage, compromise solution was chosen from the Pareto sets through various multi-criteria decision making (MCDM) methods. This study aims to compare performances of the NSGA-II and the MODE via various MCDM methods using three real data sets taken from different fields. The novelty of this paper is applying the MCDM methods to the Pareto solution set to choose a compromise solution by taking into account the Entropy weights of responses primarily. Afterwards, closeness of the compromise solution to the ideal solution using the mean absolute error (MAE) and the root mean square error (RMSE) metrics is calculated for decision making on the performance of the MOO methods. The results showed that compromise solution of the MODE is closer to the ideal solution than the NSGA-II according to the MAE and RMSE metrics. As a result, the MODE outperforms the NSGA-II.
Yazarlar (3)
1
Serhan Tuncel
ORCID: 0000-0002-3598-0331
2
Ozlem Turksen
ORCID: 0000-0002-5592-1830
3
Nimet Yapıcı Pehlivan
Anahtar Kelimeler
MCDM
MODE
Multi-response Experimental Data
NSGA-II
Kurumlar
Ankara Üniversitesi
Ankara Turkey
Hacettepe Üniversitesi
Ankara Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Sigma Journal of Engineering and Natural Sciences
Q3
OA
SJR Skoru0,206
H-Index16
YayıncıYildiz Technical University
ÜlkeTurkey
Engineering (miscellaneous) (Q3)
Computational Mechanics (Q4)
Energy (miscellaneous) (Q4)
Mechanics of Materials (Q4)
Metrikler
1
Atıf
3
Yazar
4
Anahtar Kelime