Name
Title | ||
|---|---|---|
Research of multi-modal function optimization based on multi-agent immune genetic algorithm. |
Abstract | ||
|---|---|---|
To the deficiency of conventional genetic algorithm in solving multi-modal function optimization problem, the Multi-Agent technology in combination with immune principle was presented, in this new algorithm, the immune Agent dominant operator was provided, the operator can acquire the environment information from the evolution procedure, then real-time adjust and control the evolution operating, in order to find out the global optimum value quickly and efficiently. The simulation experiments indicates that the algorithm improves the deficiency of the genetic algorithm and is better than the conventional genetic algorithm, has the well ability of global and local search as wello © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/EMEIT.2011.6023759 | EMEIT |
Keywords | Field | DocType |
genetic algorithm,immune evolution,multi-agent,multi-modal optimization,real time,genetic algorithms,local search,multi agent systems,artificial immune systems,simulation experiment | Genetic operator,Mathematical optimization,Artificial immune system,Computer science,Meta-optimization,Genetic representation,Cultural algorithm,Population-based incremental learning,Quality control and genetic algorithms,Genetic algorithm | Conference |
Volume | Issue | Citations |
6 | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shurong Liu | 1 | 0 | 0.34 |
| Xiangping Meng | 2 | 18 | 4.95 |
| Wei Pang | 3 | 2 | 2.14 |
| Hui Wang | 4 | 386 | 27.33 |