Scopus
YÖKSİS DOI Eşleşti
SJR Q2
Integration search strategies in tree seed algorithm for high dimensional function optimization
International Journal of Machine Learning and Cybernetics · Şubat 2020
YÖKSİS Kayıtları
Integration search strategies in tree seed algorithm for high dimensional function optimization
International Journal of Machine Learning and Cybernetics · 2020 SCI
Doç. Dr. AHMET CEVAHİR ÇINAR →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Integration search strategies in tree seed algorithm for high dimensional function optimization
2020 ISSN: 1868-8071 SCI Q2
Doç. Dr. AHMET CEVAHİR ÇINAR →
Vortex search optimization algorithm for training of feed-forward neural network
2021 ISSN: 1868-8071 SCI-Expanded Q2
Doç. Dr. TAHİR SAĞ →
Makale Bilgileri
ISSN18688071
Yayın TarihiŞubat 2020
Cilt / Sayfa11 · 249-267
Scopus ID2-s2.0-85067815829
Özet
The tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and seeds correspond to the possible solutions of the optimization problem on the search space. By using this model, the continuous optimization problems with lower dimensions are solved effectively, but its performance dramatically decreases on solving higher dimensional optimization problems. In order to address this issue in the basic TSA, an integration of different solution update rules are proposed in this study for solving high dimensional continuous optimization problems. Based on the search tendency parameter, which is a peculiar control parameter of TSA, five update rules and a withering process are utilized for obtaining seeds for the trees. The performance of the proposed method is investigated on basic 30-dimensional twelve numerical benchmark functions and CEC (congress on evolutionary computation) 2015 test suite. The performance of the proposed approach is also compared with the artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm, pure random search algorithm and differential evolution variants. Experimental comparisons show that the proposed method is better than the basic method in terms of solution quality, robustness and convergence characteristics.
Yazarlar (4)
1
Imral Gungor
2
Bulent Gursel Emiroglu
3
Ahmet Cevahir Cinar
4
Mustafa Kiran
Anahtar Kelimeler
Metaheuristic algorithms
Nonlinear global optimization
Swarm intelligence
Withering process
Kurumlar
Information Technologies Department
Ankara Turkey
Kirikkale Üniversitesi
Kirikkale Turkey
Konya Division
Konya Turkey
Konya Technical University
Konya Turkey
Scimago Dergi (ISSN Eşleşmesi)
International Journal of Machine Learning and Cybernetics
Q2
SJR Skoru0,699
H-Index79
YayıncıSpringer Science + Business Media
ÜlkeUnited States
Artificial Intelligence (Q2)
Computer Vision and Pattern Recognition (Q2)
Software (Q2)
Metrikler
29
Atıf
4
Yazar
4
Anahtar Kelime