Name
Abstract | ||
|---|---|---|
In recent years, many researchers focus on metha-heuristics as a method to large-scale and complicated optimization problems. in these problems, an optimization method requires versatility and applicability to a characteristic of design space by combining appropriate global and local searches. Multi-Agent Optimization (MAO) is a method based on Multi-Agent System, and it has been proposed to satisfy these requirements. in this method, agents which represent solution candidates evolve by their autonomous actions and interaction with each other. through these features, it is expected that MAO can perform global search with whole agents and local search with each agent efficiently. However, it is not clear what parameters are more effective to the evolution of MAO. in this paper, an attempt is made to verify the applicability of MAO to optimization problems by clarifying effects of its parameters with numerical experiments. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICGEC.2012.59 | Genetic and Evolutionary Computing |
Keywords | Field | DocType |
multi-agent optimization method,complicated optimization problem,multi-agent optimization,numerical experiment,design space,autonomous action,recent year,optimization method,global search,evolution mechanism,multi-agent system,local search,multi agent systems | Probabilistic-based design optimization,Mathematical optimization,Computer science,Meta-optimization,Multi-agent system,Multi-swarm optimization,Local search (optimization),Engineering optimization,Optimization problem,Metaheuristic | Conference |
ISSN | ISBN | Citations |
1949-4653 | 978-1-4673-2138-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Koichiro Nakatsu | 1 | 2 | 1.39 |
| H. Furuta | 2 | 23 | 6.55 |
| Kyosuke Takahashi | 3 | 0 | 0.34 |
| Ken Ishibashi | 4 | 0 | 1.01 |
| Masahiro Uchida | 5 | 7 | 0.91 |