Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal?

By Kokolo Ikeda, Hajime Kita, Shigenobu Kobayashi. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001

358 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/4eb623e97bcfeaefaa6dea474d4524bba178f905

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Citations in the corpus

A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “CDAS-dominance [26], α-dominance [27], fuzzy Pareto dominance [28], L-dominance [29], θ -dominance [30], and so on [31]–[33] have been experimentally verified to be more”
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, background): “Increasing research attention has therefore been paid to tackling MaOPs in recent years, as it has been shown that MaOPs cannot be solved efficiently using MOEAs developed for solving MOPs with two or three objectives [14]–[17].”
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, background): “However, according to the aforementioned second challenge, the active diversity promotion leads to a detrimental effect on the convergence toward the EF [13], [22], [23], due to the presence of dominance resistant solutions [24].”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, background): “This leads to difficulties in producing offspring that dominate poorly performing, but still non-dominated, members in the population — a phenomenon termed dominance resistance (Hanne, 2001; Ikeda et al., 2001; Purshouse and Fleming, 2007).”
Scalable multi-objective optimization test problems (2002, background): “Another study ( Kokolo, Kita, and Kobayashi, 2001 ) has also recognized that this feature of problems can cause MOEAs difficulty in finding the true Pareboptima1 solutions.”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, background): “MOEAs have been shown to perform well on a wide range of MOPs with two or three objectives; however, MOEAs have experienced substantial difficulties when they are adopted to tackle MOP s with more than three objectives [2]–[5], often referred to a s the many-objective problems (MaOPs) nowadays.”
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, background): “with two or three objectives, while those with more than thre e objectives are known as many-objective optimization probl ems (MaOPs) [1]–[4].”
Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs (2007, background): “Another strategy that relaxes Pareto dominance is α-domination proposed by Ikeda et al. [ 7 ] to strengthen selection.”
On the Evolutionary Optimization of Many Conflicting Objectives (2007, background): “For selection-for-variation, the term refers to a large proportion of such solutions in the combined prevariation and postvariation population (which would include the definition given in [37]).”
Evolutionary many-objective optimization: A short review (2008, background): “[35] where the angle of the dominance region was widened as in Fig.”