Reference point based multi-objective optimization using evolutionary algorithms

By Kalyanmoy Deb, J. Sundar, Udaya Bhaskara, Rao N, Shamik Chaudhuri. Annual Conference on Genetic and Evolutionary Computation, 2006

881 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ced0f69e72037218918c27a9e8463bb0ac7ccb11

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Citations in the corpus

• Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II (2009, methodology): “A number of MOEAs adopt this idea for their fitness assignment to some extent [29]–[41].”
• Understanding knee points in bicriteria problems and their implications as preferred solution principles (2011, methodology): “Knee points are well-recognized by multi-objective optimization researchers (Das 1999, Miettinen 1999, Branke et al. 2004, Mattson et al. 2004, Deb and Sundar 2006, Rachmawati and *Corresponding author.”
• Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, neutral): “Some interactive approaches (e.g. Hwang et al., 1979) have been developed in order to interactively explore the Pareto space without having to fully compute it in advance, thus mitigating the associated computational burden (e.g. Deb et al., 2006; Thiele et al., 2009).”
• A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, methodology): “proposed an MOEA for finding knee regions, termed KR-NSGA-II [46] by extending the reference point based MOEA, called R-NSGA-II [47].”
• Many-Objective Evolutionary Algorithms (2015, background): “Goal point R-NSGA-II [Deb et al. 2006] DTLZ(10) Goal points RD-NSGA-II [Deb and Kumar 2007] DTLZ(10) Reference direction IEA with preference polyhedron [Gong et al. 2013a] DTLZ(5) Preference polyhedron 8.1.2.”
• Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead (2008, methodology): “(2003), Tan et al. (2005), Koksalan and Pamuk (2007), and Deb and Sundar (2006) .”
• A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, background): “DTLZ1–DTLZ6 [55] test problems with 4–10 objectives demonstrated that both R-MEAD2-TCH and R-MEAD2-PBI significantly outperform R-NSGA-II [128] on most of the test problems.”
• A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “In comparison with the reference point-based preference articulation methods [39], [58]–[60], reference vector-based preference articulation is more intuitive, as each reference vec-”
• An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “In addition, since the proposed AR-MOEA is also highly related to preference-based MOEAs [64]–[67] due to preference guided search mechanisms, we would like to further investigate how to perform preference articulation and decision making using it by specifying regions of interests using the referen”
• Pymoo: Multi-Objective Optimization in Python (2020, background): “For instance, a Steady-State NSGA-II [34] can be initialized by setting the number of offspring NSGA-II [12] RNSGA-II [14] NSGA-III [10, 26, 4] UNSGA-III [43] RNSGA-III [47] MOEAD [52] to 1 .”
• Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction (2011, background): “For this purpose, a reference point approach [36], a reference direction approach [37], “light beam” approach [38] etc.”
• An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (2014, background): “A number of MCDM-based EMO methodologies are already devised for this purpose and results in as large as ten-objective problems have shown to perform well [20]–[23].”