A survey on multi-objective evolutionary algorithms for many-objective problems

By Christian von Lücken, Benjamín Barán, Carlos A. Brizuela. Computational optimization and applications, 2014

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Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, background): “Previous research in many-objective optimisation [55], [34] has shown that many-objective problems are particularly challenging because of the dominance resistance phenomenon, i.”
Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (2017, background): “to as many-objective problems [12], [34], [35].”
Grasshopper optimization algorithm for multi-objective optimization problems (2017, background): “Some of these difficulties/features are: dynamicity [12–14], uncertainty [15], constraints, [16], multiple objectives [17], and many objectives [18].”
A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization (2018, background): “For more details about these algorithms and challenges in solving problems with more than three objectives, see [23], [29], [45].”
Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “Readers are referred to [29] and [30] for a detailed survey.”
Many-Objective Evolutionary Algorithms (2015, background): “—Completeness: Von Lücken et al. [2014] conducted a good review over 112 references.”