Abstract
This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular we focus on problems with objective replacement where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation we suggest the inheritance strategy. When objective replacement occurs this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span.
| Original language | English |
|---|---|
| Pages (from-to) | 267-293 |
| Number of pages | 27 |
| Journal | Artificial Intelligence Review |
| Volume | 23 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - May 2005 |
| Externally published | Yes |
Keywords
- Multi-objective genetic algorithms
- Multi-objective optimization
- Multi-objective problems
- Non-stationary environment