eprintid: 11673
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/16/73
datestamp: 2023-11-10 03:26:11
lastmod: 2023-11-10 03:26:11
status_changed: 2023-11-10 01:15:49
type: article
metadata_visibility: show
creators_name: Truong, K.H.
creators_name: Nallagownden, P.
creators_name: Baharudin, Z.
creators_name: Vo, D.N.
title: A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems
ispublished: pub
keywords: Biology; Distributed power generation; Functions; Global optimization; Local search (optimization); Structural design; Systems engineering, Chaotic local searches; Distributed generation units; Global optimization problems; Opposition-based learning; Optimization problems; Radial distribution networks; Structural design optimization; Symbiotic organisms search, Structural optimization
note: cited By 65
abstract: This study proposes an improved version of the Symbiotic Organisms Search (SOS) algorithm called Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS). This improved algorithm integrated Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies with SOS for a better quality solution and faster convergence. To demonstrate and validate the new algorithm's effectiveness, the authors tested QOCSOS with twenty-six mathematical benchmark functions of different types and dimensions. In addition, QOCSOS optimized placements for distributed generation (DG) units in radial distribution networks and solved five structural design optimization problems, as practical optimization problems challenges. Comparative results showed that QOCSOS provided more accurate solutions than SOS and other methods, suggesting viability in dealing with global optimization problems. © 2019 Elsevier B.V.
date: 2019
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061439667&doi=10.1016%2fj.asoc.2019.01.043&partnerID=40&md5=d8ff5abe7c772694a19145bf03b6897c
id_number: 10.1016/j.asoc.2019.01.043
full_text_status: none
publication: Applied Soft Computing Journal
volume: 77
pagerange: 567-583
refereed: TRUE
issn: 15684946
citation:   Truong, K.H. and Nallagownden, P. and Baharudin, Z. and Vo, D.N.  (2019) A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems.  Applied Soft Computing Journal, 77.  pp. 567-583.  ISSN 15684946