%P 567-583
%A K.H. Truong
%A P. Nallagownden
%A Z. Baharudin
%A D.N. Vo
%I Elsevier Ltd
%V 77
%T A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems
%R 10.1016/j.asoc.2019.01.043
%D 2019
%J Applied Soft Computing Journal
%L scholars11673
%O cited By 65
%X 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.
%K 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