Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs

By Hiroyuki Sato, H. Aguirre, Kiyoshi Tanaka. International Conference on Evolutionary Multi-Criterion Optimization, 2007

310 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/fbedf135003bb6e0cc0c14517dd9c5a83906a74a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

In Industrial and Manufacturing Engineering

Citations in the corpus

Many-Objective Evolutionary Algorithms (2015, background): “On the one hand, to mitigate the adverse impact of DR, researchers have proposed several variants of the Pareto dominance to enhance the selection pressure toward the PF [Laumanns et al. 2002; Sato et al. 2007].”
A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “These S i values corresponds to the maximum expansion of the dominance area [87].”
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015, methodology): “Due to the ineffectiveness of the Pareto dominance in manyobjective optimization [39], many modifications have been made to improve the effectiveness of the Pareto dominance, such as the ε-dominance [40]–[42], the changing dominance area [43], the ranking method [44], and the fuzzy-Paretodominance [”
A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “To be specific, the parameters S in CDAS is set to 0.39, the parameter φi in GPO is set to 0.34, the parameter div in grid dominance is set to 5, the parameter k in (1−k)-dominance is set to 0.25, the number of reference vectors used by θ -dominance is set to 3, and the parameter θ in θ -dominance i”
Evolutionary many-objective optimization: A short review (2008, background): “One approach based on this idea is to modify Pareto dominance in order to decrease the number of non-dominated solutions in each population [12].”
Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (2017, background): “problems, various approaches have been proposed such as the modification of the Pareto dominance relation [17] and the introduction of an additional ranking mechanism [18]–[21].”
Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization (2013, methodology): “While the use of goals does rely on Pareto-dominance comparison at the level of the individual goal, the presence of multiple goals can significantly mitigate the comparability issues observed when scaling the standard dominance relation.”
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, background): “So far, many alternative preference relations have been proposed for many-objective optimization, such as average and maximum ranking [12], favor relation [13], fuzzy Pareto dominance [14], expansion relation [15], preference order ranking [16], θ -dominance [17], and so on [18]–[20].”