HIGHLIGHTS
SUMMARY
Multi-objective optimization problems (MOPs)-i.e., problems where several objectives have to be optimized concurrently -naturally arise in many applications (e_g, ). In the area of evolutionary multi-objective optimization (EMO), many performance indicators have been proposed that propagate optimal approximations of the Pareto front (e_g, ). In set-scalarization methods, rather than focusing on the improvement of single points of the approximation set, the focus is on the optimization of a fixed cardinality set as an entity concerning a set-based indicator, e_g, the hypervolume indicator. The so-called indicator-based MOEAs (e_g, ) use . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.