An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

By Kalyanmoy Deb, Himanshu Jain. IEEE Transactions on Evolutionary Computation, 2014

6,084 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/7f46f1b3c2c3919de17b07eefa7dab889a3d1eca

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Citations in the corpus

• An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach (2014, methodology): “We also propose the use of PBI metric (instead of the Tchebycheff metric), as PBI metric was found to work better in the original study [1].”
• A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem (2017, methodology): “In contrast to other many-objective evolutionary algorithms, e.g., NSGA-III [19] and MOEA/D [20], which use a set of reference points and weights, RVEA adopts a set of reference vectors.”
• A Method for a Posteriori Identification of Knee Points Based on Solution Density (2018, methodology): “MOEAs can be roughly classified into four categories, namely, dominance-based methods [1], [2], [3], [4], indicators or metrics based approaches [5], [6], [7], aggregation or decomposition based methods [8], [9], [10], [11], as well as preference-based methods [12], [13], [14], [15].”
• A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, methodology): “2) Population sizing: To avoid that the generated reference points are all located along the boundary of Pareto fronts for problems with a large number of objectives, the strategy of two-layered reference points recommended in NSGA-III [35] was adopted to generate uniformly distributed weight vector”
• An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, methodology): “However, this mechanism benefits the diversity preservation, which is very important for many-objective optimization. e) In MOEA/DD, the neighborhood concept is only used for mating restriction, while it is used for both mating and update procedures in MOEA/D. 4) Differences Between MOEA/DD and NSGA”
• A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, methodology): “The distribution index of SBX and polynomial mutation are set to be 20, in addition to the NSGA-III whose distribution index needs to be specified as 30 based on its developers [8].”
• Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets (2020, methodology): “Pareto front is a set of nondominated solutions, being chosen as optimal, if no objective can be improved without sacrificing at least one other objective [32, 3].”
• An easy-to-use real-world multi-objective optimization problem suite (2020, methodology): “A number of EMOAs have been proposed, including NSGA-II [2], SPEA2 [3], IBEA [4], SMS-EMOA [5], MOEA/D [6], and NSGA-III [7].”
• Multi-Objective Evolutionary Federated Learning (2018, methodology): “, the evolutionary many-objective optimization algorithm using referencepoint based non-dominated sorting approach [23], the kneedriven evolutionary algorithm for many-objective optimization [24], and the reference vector guided evolutionary for manyobjective optimization [25].”
• A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “Accordingly, the Das and Dennis’s approach with two layers [42] is used to generate the reference vectors used in θ -dominance, NSGA-III, MOEA/DD, RVEA, and MOMBIII, where the parameters (p1, p2) controlling the numbers of reference vectors along the outer and inner layers are set to (6, 0), (3, 2),”
• A tutorial on multiobjective optimization: fundamentals and evolutionary methods (2018, methodology): “The NSGA-III (Deb and Jain 2014) algorithm is specially designed for many-objective optimization and uses a set of reference points that is dynamically updated in its decomposition.”
• A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019, methodology): “both outer boundaries and inside layers of the Pareto optimal fronts, where p1 and p2 are parameters controlling the numbers of weight vectors along the boundary of the Pareto front and inside it, respectively [11].”
• A comprehensive survey on NSGA-II for multi-objective optimization and applications (2023, result): “NSGA-III is a novel reference-point-based many-objective NSGA-II proposed by Deb and Jain (2014). The general structure of NSGA-III is similar to that of the NSGA-II. Both of them use recombination and mutation operators to generate offspring solutions, and then apply fast non-dominated sorting meth”
• A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “4) The studies MOEA/D-M2M [70], NSGA-III [40], MOEA/DD [39], and RVEA [41] provide new direction”
• Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, background): “Some popular examples of multiobjective evolutionary algorithms (MOEAs) commonly in use today include nondominated sorting genetic algorithm (NSGA-II) [11], NSGA-III [12], [13], SPEA2 [14], MOEA/D [15]–[17], and indicator-based MOEAs [18].”
• A Survey on Evolutionary Neural Architecture Search (2020, background): “Because NSGA-III [189] may fall into the small model trap (this algorithm prefers small models), Yang et al. [137] have made some improvements to the conventional NSGA-III for favoring larger models.”
• Stable Matching-Based Selection in Evolutionary Multiobjective Optimization (2014, background): “, [22] and [23], the setting used in this paper can produce more uniformly distributed solutions in the objective space.”