TY  - JOUR
EP  - 583
SN  - 15684946
PB  - Elsevier Ltd
N1  - cited By 65
TI  - A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems
SP  - 567
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061439667&doi=10.1016%2fj.asoc.2019.01.043&partnerID=40&md5=d8ff5abe7c772694a19145bf03b6897c
A1  - Truong, K.H.
A1  - Nallagownden, P.
A1  - Baharudin, Z.
A1  - Vo, D.N.
JF  - Applied Soft Computing Journal
VL  - 77
Y1  - 2019///
N2  - 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.
KW  - Biology; Distributed power generation; Functions; Global optimization; Local search (optimization); Structural design; Systems engineering
KW  -  Chaotic local searches; Distributed generation units; Global optimization problems; Opposition-based learning; Optimization problems; Radial distribution networks; Structural design optimization; Symbiotic organisms search
KW  -  Structural optimization
ID  - scholars11673
ER  -