Name
Abstract | ||
|---|---|---|
The run time for many optimisation algorithms, particularly those that explicitly consider multiple objectives, can be impractically large when applied to real world problems. This paper reports an investigation into the behaviour of Multi-Objective Particle Swarm Optimisation (MOPSO), which seeks to reduce the number of objective function evaluations needed, without degrading solution quality. By restricting archive size and strategically reducing the trial solution population size, it has been found the number of function evaluations can be reduced by 66.7% without significant reduction in solution quality. In fact, careful manipulation of algorithm operating parameters can even significantly improve solution quality. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.procs.2015.05.435 | INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2015 COMPUTATIONAL SCIENCE AT THE GATES OF NATURE |
Keywords | Field | DocType |
Multi-objective Particle Swarm Optimisation, MOPSO, computational efficiency | Particle swarm optimization,Mathematical optimization,Computer science,Population size | Conference |
Volume | ISSN | Citations |
51 | 1877-0509 | 1 |
PageRank | References | Authors |
0.36 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathew Curtis | 1 | 1 | 0.36 |
| Andrew Lewis | 2 | 15 | 4.30 |