Name
Abstract | ||
|---|---|---|
By combining the aspect of population in genetic algorithms (GAs) and the simulated annealing algorithm (SAA), a novel algorithm, called fast annealing evolutionary algorithm (FAEA), is proposed. The algorithm is similar to the annealing evolutionary algorithm (AEA), and a very fast annealing technique is adopted for the annealing procedure. By an application of the algorithm to the optimization of test functions and a comparison of the algorithm with other stochastic optimization methods, it is shown that the algorithm is a highly efficient optimization method. It was also applied in optimization of Lennard-Jones clusters and compared with other methods in this study. The results indicate that the algorithm is a good tool for the energy minimization problem. (C) 2002 John Wiley Sons, Inc. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1002/jcc.10029 | JOURNAL OF COMPUTATIONAL CHEMISTRY |
Keywords | Field | DocType |
annealing evolutionary algorithm,global optimization,Lennard-Jones clusters | Simulated annealing,Stochastic optimization,Mathematical optimization,Global optimization,Evolutionary algorithm,Computer science,Meta-optimization,Adaptive simulated annealing,Imperialist competitive algorithm,Genetic algorithm | Journal |
Volume | Issue | ISSN |
23 | 4 | 0192-8651 |
Citations | PageRank | References |
12 | 1.28 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wensheng Cai | 1 | 68 | 10.88 |
| Xueguang Shao | 2 | 63 | 10.81 |