Name
Abstract | ||
|---|---|---|
The paper introduces an evolutionary swarm model (ESM), based on the model, an evolutionary swarm algorithm (ESA) is designed out using five elements. In this work, the performance of ESA is tested with 5 multivariable benchmark functions, and compared with the other optimization algorithms. The simulation results show that the algorithm has an excellent performance in the global optimization, and can be efficiently employed to solve the optimization problem for the multimodal function with high dimensionality. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.79 | ICNC (5) |
Keywords | Field | DocType |
numerical function optimization,multivariable benchmark functions,evolutionary computation,multimodal function optimization,particle swarm optimisation,global optimization,numerical analysis,multimodal function,evolution algorithm,multivariable benchmark function,optimization algorithm,excellent performance,optimization problem,evolutionary swarm optimization algorithm,evolutionary swarm algorithm,high dimensionality,simulation result,swarm algorithm,evolutionary swarm model,data mining,optimization,benchmark testing,particle swarm optimization,algorithm design and analysis | Continuous optimization,Mathematical optimization,Global optimization,Computer science,Test functions for optimization,Meta-optimization,Evolutionary computation,Multi-swarm optimization,Artificial intelligence,Imperialist competitive algorithm,Machine learning,Metaheuristic | Conference |
Volume | ISBN | Citations |
5 | 978-0-7695-3736-8 | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyan Quan | 1 | 15 | 2.15 |
| xinling shi | 2 | 74 | 15.34 |