RT Journal Article
T1 Performance assessment of population-based multiobjective optimization algorithms using composite indicators
A1 Saborido Infantes, Rubén
A1 Ruiz, Ana B.
A1 González-Gallardo, Sandra
A1 Luque-Gallego, Mariano
A1 Borrego-Ortega, Antonio
K1 Optimización matemática
AB The performance of population-based multiobjectiveoptimization algorithms is usually evaluated using indicatorsassessing the quality of the approximation set generated accordingto convergence, cardinality, spread, and uniformity (thecombination of the last two known as diversity). Since not allquality indicators can capture all these properties, we proposeto aggregate already-existing indicators into a single measureinforming about the algorithm’s performance from a generalperspective. To synthesize the desired quality indicators, we buildthree composite quality indicators (weak, strong, and mixed)based on the reference point approach. This approach enablesthe use of desirable value ranges for the aggregated qualityindicators, defined by aspiration and reservation levels, thatallow knowing which algorithms perform better, within, or worsethan the desired limits. Each of the composite quality indicatorsproposed enables a different compensation degree among theaggregated indicators, and their joint use permits a deep insightinto the algorithms’ performance. In addition, we show thatthe weak and mixed composite indicators are Pareto-compliant,and the strong one is weakly Pareto-compliant if at least oneof the aggregated indicators is Pareto-compliant. Finally, wedemonstrate the benefits of our proposal when comparing manypopulation-based algorithms on three-, five-, and eight-objectiveoptimization problems.
PB IEEE
YR 2025
FD 2025-02
LK https://hdl.handle.net/10630/39444
UL https://hdl.handle.net/10630/39444
LA eng
NO R. Saborido, A. B. Ruiz, S. González-Gallardo, M. Luque and A. Borrego, "Performance Assessment of Population-Based Multiobjective Optimization Algorithms Using Composite Indicators," in IEEE Transactions on Evolutionary Computation, doi: 10.1109/TEVC.2025.3544412
NO Funding for open access charge: Universidad de Málaga/CBUA.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026