Name
Title | ||
|---|---|---|
A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems. |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.asoc.2019.01.043 | Applied Soft Computing |
Keywords | Field | DocType |
Chaotic local search,Distributed generation,Global optimization problems,Meta-heuristic quasi-opposition-based learning,Symbiotic organisms search | Convergence (routing),Mathematical optimization,CLs upper limits,Search algorithm,Distribution networks,Distributed generation,Chaotic,Optimization problem,Mathematics,Global optimization problem | Journal |
Volume | ISSN | Citations |
77 | 1568-4946 | 2 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khoa H. Truong | 1 | 3 | 1.38 |
| Perumal Nallagownden | 2 | 6 | 3.22 |
| Zuhairi Baharudin | 3 | 4 | 2.47 |
| Dieu N. Vo | 4 | 4 | 2.45 |