tb_moo_03.pdf (142.91 kB)
A Simplified Artificial Life Model for Multiobjective Optimisation:\ A Preliminary Report
conference contribution
posted on 2023-05-26, 10:07 authored by Berry, A, Vamplew, PRecent research in the field of Multiobjective\ Optimisation (MOO) has been focused on achieving the\ Pareto optimal front by explicitly analysing the\ dominance level of individual solutions. While such\ approaches have produced good results for a variety of\ problems, they are computationally expensive due to the\ complexities of deriving the dominance level for each\ solution against the entire population. TB_MOO\ (Threshold Based Multiobjective Optimisation) is a new\ artificial life approach to MOO problems that does not\ analyse dominance, nor perform any agent-agent\ comparisons. This reduction in complexity results in a\ significant decrease in processing overhead. Results\ show that TB_MOO performs comparably, and often\ better, than its more complicated counter-parts with\ respect to distance from the Pareto optimal front, but is\ slightly weaker in terms of distribution and extent.
History
Publication status
- Published
Event title
2003 IEEE Congress on Evolutionary ComputationEvent Venue
Canberra, AustraliaDate of Event (Start Date)
2003-12-08Date of Event (End Date)
2003-12-12Repository Status
- Open
Usage metrics
Categories
No categories selectedLicence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC