Name
Abstract | ||
|---|---|---|
The scaling properties of multimodal optimization methods have seldom been studied, and existing studies often concentrated on the idea that all local optima of a multimodal function can be found and their number can be estimated a priori. We argue that this approach is impractical for complex, high-dimensional target functions, and we formulate alternative criteria for scalable multimodal optimization methods. We sug- gest that a scalable niching method should return the more local optima the longer it is run, without relying on a fixed number of expected optima. This can be fulfilled by sequential and semi- sequential niching methods, several of which are presented and analyzed in that respect. Results show that, while sequential local search is very successful on simpler functions, a clustering-based particle swarm approach is most successful on multi-funnel func- tions, offering scalability even under deceptive multimodality, and denoting it a starting point towards effective scalable niching. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CEC.2010.5585916 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
particle swarm optimisation,clustering-based particle swarm approach,deceptive multimodality,multifunnel functions,multimodal function,multimodal optimization methods,niching methods,scaling properties,sequential local search | Particle swarm optimization,Mathematical optimization,Computer science,Multimodal function,Local optimum,A priori and a posteriori,Artificial intelligence,Local search (optimization),Cluster analysis,Machine learning,Benchmark (computing),Scalability | Conference |
ISBN | Citations | PageRank |
978-1-4244-6909-3 | 3 | 0.38 |
References | Authors | |
21 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel Kronfeld | 1 | 74 | 6.67 |
| Andreas Zell | 2 | 1419 | 137.58 |