TY - GEN
T1 - An Assignment Problem Formulation for Dominance Move Indicator
AU - Lopes, Claudio Lucio Do Val
AU - Martins, Flavio Vinicius Cruzeiro
AU - Wanner, Elizabeth F.
PY - 2020/9/3
Y1 - 2020/9/3
N2 - Dominance move (DoM) is a binary quality indicator to compare solution sets in multiobjective optimization. The indicator allows a more natural and intuitive relation when comparing solution sets. Like the \epsilon-indicators, it is Pareto compliant and does not demand any parameters or reference sets. In spite of its advantages, the combinatorial calculation nature is a limitation. The original formulation presents an efficient method to calculate it in a bi-objective case only. This work presents an assignment formulation to calculate DoM in problems with three objectives or more. Some initial experiments, in the bi-objective space, were done to show that DoM has a similar interpretation as \epsilon-indicators, and to show that our model formulation is correct. Next, other experiments, using three dimensions, were also done to show how DoM could be compared with other indicators: inverted generational distance (IGD) and hypervolume (HV). The assignment formulation for DoM is valid not only for three objectives but for more. Finally, there are some strengths and weaknesses, which are discussed and detailed.
AB - Dominance move (DoM) is a binary quality indicator to compare solution sets in multiobjective optimization. The indicator allows a more natural and intuitive relation when comparing solution sets. Like the \epsilon-indicators, it is Pareto compliant and does not demand any parameters or reference sets. In spite of its advantages, the combinatorial calculation nature is a limitation. The original formulation presents an efficient method to calculate it in a bi-objective case only. This work presents an assignment formulation to calculate DoM in problems with three objectives or more. Some initial experiments, in the bi-objective space, were done to show that DoM has a similar interpretation as \epsilon-indicators, and to show that our model formulation is correct. Next, other experiments, using three dimensions, were also done to show how DoM could be compared with other indicators: inverted generational distance (IGD) and hypervolume (HV). The assignment formulation for DoM is valid not only for three objectives but for more. Finally, there are some strengths and weaknesses, which are discussed and detailed.
KW - evolutionary algorithms
KW - exact method
KW - multiobjective optimization
KW - performance assessment
KW - quality indicator
UR - https://ieeexplore.ieee.org/document/9185597
UR - http://www.scopus.com/inward/record.url?scp=85092027584&partnerID=8YFLogxK
U2 - 10.1109/CEC48606.2020.9185597
DO - 10.1109/CEC48606.2020.9185597
M3 - Conference publication
AN - SCOPUS:85092027584
T3 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
BT - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
PB - IEEE
T2 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020
Y2 - 19 July 2020 through 24 July 2020
ER -