Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization

By Rui Wang, Robin Charles Purshouse, Peter John Fleming. IEEE Transactions on Evolutionary Computation, 2013

402 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/83578a9227fdc789d61a57b83e2fbe41ca182bf6

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Citations in the corpus

Many-Objective Evolutionary Algorithms (2015, background): “—A posteriori algorithms (selection after search): The preference information is introduced after running MaOEAs and obtaining a solution set which approximates the real PF [Purshouse et al. 2011; Wang et al. 2013c; 2012; 2013a].”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “assignment based NSGA-II [18], grid-based evolutionary algorithm [19], preference-inspired coevolutionary algorithm [20], many-objective evolutionary algorithm based on directional diversity and favorable convergence [21], and knee point driven evolutionary algorithm (KnEA) [22].”
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, background): “For example, some researchers attempted to solve MaOPs by using a reduced set of objectives [32], [33], while others suggested to use interactive user preferences [34] or reference points [35] during the search.”
Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “We have found that using fine-tuned parameter settings, PICEA-g and HypE could have comparable performance with MOEA/D-LWS.”
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016, background): “Some other studies with this regard can be referred in [29] and [60]–[62].”
Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization (2020, methodology): “MOEAs for MaOPs can roughly be divided into four categories [8], [9]; namely, 1) dominance relationship modification-based methods [10]–[13]; 2) indicators-based methods [14]–[16]; 3) aggregation-based (also known as decomposition based) [17]–[19]; or 4) preference-based approaches [20]–[23].”
A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization (2015, methodology): “nondominance-based methods such as -dominance [8], griddominance [32], dominance using preferred goals [33] and use of secondary ranking schemes for diversity manage-”
A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, background): “In other words, HypE, PICEA-g, and SPEA2+SDE use the same population size settings as NSGA-III and SPEA/R. 3) Stopping Criterion and the Number of Executions: Each algorithm was terminated after a prespecified number of generations.”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, methodology): “MOEA/IGD-NS [36] 2016 Multi-objective evolutionary algorithm based on an enhanced inverted generational distance metric Many-Objective Genetic Algorithms HypE [37] 2011 Hypervolume-based estimation algorithm PICEA-g [38] 2013 Preference-inspired coevolutionary algorithm with goals GrEA [39] 2013 Gri”
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015, background): “Such studies can be divided into two classes, interactive [33]–[35] and noninteractive [36]–[38].”
A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization (2017, background): “It should be noted that, various fine-grained methods proposed in [14], [45], and [56] can be used to assign fitness values for individuals.”
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, background): “Take MOEA/D for instance, it normally could approach the PF very well even if in the high-dimensional objective space, but it struggles to maintain the diversity in many-objective optimization, usually failing to achieve a good coverage of PF [20], [26], [43].”
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”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, background): “For example, some researchers propose to use interactive user preferences [47] or reference points [48] during the search while others suggest to solve MaOPs by using a reduced set of objectives [49]–[51].”