Name
Abstract | ||
|---|---|---|
Particle Swarm Optimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested over 12 benchmark test functions and compared with basic PSO and two other algorithms known as Gravitational search algorithm (GSA) and Biogeography based Optimization (BBO). The result reveals that ABF-PSO will be a competitive variant of PSO. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-981-10-3322-3_2 | PROCEEDINGS OF SIXTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2016), VOL 1 |
Keywords | DocType | Volume |
Meta-heuristic optimization techniques,Particle swarm optimization algorithm,Swarm intelligence,Acceleration coefficients,Nature inspired algorithm | Conference | 546 |
ISSN | Citations | PageRank |
2194-5357 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siddhi Kumari Sharma | 1 | 0 | 0.34 |
| Rajendra Kumar Sharma | 2 | 35 | 9.62 |