Name
Abstract | ||
|---|---|---|
There are many features of optimisation problems that can influence the difficulty for search algorithms. This paper investigates the steepness of gradients in a fitness landscape as an additional feature that can be linked to difficulty for particle swarm optimisation (PSO) algorithms. The performances of different variations of PSO algorithms on a range of benchmark problems are considered against average estimations of gradients based on random walks. Results show that all variations of PSO failed to solve problems with estimated steep gradients in higher dimensions. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2464576.2464582 | GECCO (Companion) |
Keywords | Field | DocType |
average estimation,different variation,pso algorithm,optimisation problem,fitness landscape,benchmark problem,pso failure,particle swarm optimisation,higher dimension,estimated steep gradient,additional feature | Particle swarm optimization,Mathematical optimization,Fitness landscape,Search algorithm,Random walk,Computer science,Artificial intelligence,Machine learning,Gradient estimation | Conference |
Citations | PageRank | References |
3 | 0.41 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Katherine Malan | 1 | 162 | 12.77 |
| Andries P. Engelbrecht | 2 | 660 | 61.64 |