A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

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A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, background): “A large number of MOEAs have been proposed to find local regions or points of interest in the Pareto optimal front [38]–[40].”
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015, background): “In some specific applications, decision makers only care about a small region of the entire Pareto front (PF) [31].”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, positive): “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).”
Many-Objective Evolutionary Algorithms (2015, methodology): “The preference-based evolutionary algorithm (PBEA) [Thiele et al. 2009] takes into account the preference information by integrating an achievement function that relies on the reference point.”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “In this sense, RVEA differs from most existing reference point based MOEAs [58]–[60] in that these algorithms use dominance and preference to search for preferred subset of the PF only.”