Empirical comparison of four MOEAs on bi-objective stochastic MKP with chance constraints and dynamic capacities shows differences in behavior across uncertainty levels and change frequencies.
Spea2: Improving the strength pareto evolutionary algorithm for multiobjective optimization.Evolutionary methods for design, optimization, and control, pages 95–100
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
On the Use of Bi-Objective Evolutionary Algorithms for the Stochastic MKP under Dynamic Constraints
Empirical comparison of four MOEAs on bi-objective stochastic MKP with chance constraints and dynamic capacities shows differences in behavior across uncertainty levels and change frequencies.