Evaluating the -Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions

By Kalyanmoy Deb, Manikanth Mohan, Shikhar Mishra. Evolutionary Computation, 2005

658 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1a382ba4d8995c8d0a415f3d0602b9080db50038

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

Citations in the corpus

Reference point based multi-objective optimization using evolutionary algorithms (2006, methodology): “Moreover, the use of the -based selection strategy (which is also similar to the -dominance strategies suggested elsewhere [17, 18]) ensures a spread of solutions near the preferred Pareto-optimal regions.”
Localized Weighted Sum Method for Many-Objective Optimization (2018, neutral): “, -dominancebased algorithm (DEA) [6] shift-density-based evolutionary algorithm (SDEA) [7], grid-based evolutionary algorithm (GrEA) [8] and 2) Performance indicator based, e.”
Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies (2024, positive): “Other algorithms that fall into this class are the Pareto Archived Evolution Strategy (PAES) [52] and the ǫ-MOEA [24], which partition the objective function space into grid cells to measure diversity.”
Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization (2013, background): “This may be due to the fact that -MOEA is not effective in obtaining extreme solutions on the Pareto front [34], [43] or perhaps the value is set inappropriately.”
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016, background): “Up to now, many alternative preference relations, such as favor relation [15], dominance [16], [17], fuzzy Pareto dominance [18], [19], preference order”
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, background): “Accordingly, the originalM -dimensional objective space will be divided into divM hyperboxes.”
An efficient multiobjective differential evolution algorithm for engineering design (2009, methodology): “[9], [10] proposed a steady-state MOEA, -MOEA, based on the -dominance concept and efficient parent and archive update strategies to MOPs.”
Many-Objective Evolutionary Algorithms (2015, background): “proposed a steady-state algorithm named -MOEA [Deb et al. 2005].”
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point (2009, background): “Extreme points are not generally preferred as claimed in [7], since the density of points does not depend on the position on the front but only on the gradient at the respective point.”
Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization (2012, background): “, by the use of -dominance as in [8], [29], [43], and [44]).”
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 [”
Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization (2014, background): “In addition, several classical concepts which are not particularly designed for many-objective problems, such as ǫ-dominance [54] and fuzzy Pareto dominance [49], have also been shown to provide competitive results [19], [28], [50].”