Name
Title | ||
|---|---|---|
An effective multiagent evolutionary algorithm integrating a novel roulette inversion operator for engineering optimization |
Abstract | ||
|---|---|---|
Multiagent systems have been studied and widely used in the field of artificial intelligence and computer science to catalyze computation intelligence. In this paper, a multiagent evolutionary algorithm called RAER based on the ERA multiagent modeling pattern is proposed, where ERA has the same architecture as Swarm including three parts of Environment, Reactive rules and Agents. RAER integrates a novel roulette inversion operator (RIO) proposed in this paper and theoretically proved to conquer the irrationality of the inversion operator (IO) designed by John Holland when used for real code stochastic optimization algorithms. Experiments for numerical optimization of 4 benchmark functions show that the RIO operator bears better functioning than IO operator. And experiments for numerical optimization of 12 benchmark functions are used to examine the performance and scalability of RAER along the problem dimensions ranging 20-10000, results indicate that RAER outperforms other comparative algorithms significantly. Also, two engineering optimization problems of a stable linear system approximation and a welded beam design are used to examine the applicability of RAER. Results show that RAER has better search ability and faster convergence speed. Especially for the approximation problem, REAR can find the proper optima belonging to different fixed search areas, which is significantly better than other algorithms and shows that RAER can search the problem domains more thoroughly than other algorithms. Hence, RAER is efficient and practical. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.amc.2009.01.048 | Applied Mathematics and Computation |
Keywords | Field | DocType |
design optimization,evolutionary algorithm,numerical optimization,multiagent system,linear system,inversion operator,artificial intelligent,computational intelligence,optimization problem,stochastic optimization | Stochastic optimization,Mathematical optimization,Linear system,Evolutionary algorithm,Algorithm,Operator (computer programming),Search problem,Engineering optimization,Stochastic programming,Mathematics,Numerical linear algebra | Journal |
Volume | Issue | ISSN |
211 | 2 | Applied Mathematics and Computation |
Citations | PageRank | References |
5 | 0.52 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junling Zhang | 1 | 5 | 0.52 |
| Chang-Yong Liang | 2 | 275 | 20.92 |
| Yongqing Huang | 3 | 5 | 0.52 |
| Jian Wu | 4 | 71 | 6.69 |
| Shanlin Yang | 5 | 787 | 60.80 |