A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization
By Md. Asafuddoula, Tapabrata Ray, Ruhul Amin Sarker. IEEE Transactions on Evolutionary Computation, 2015
387 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/888bcf04c7927147ea1a8ca04cb1e71cad62bcaf
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Citations in the corpus
- Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, methodology): “To relax the definition of feasibility, the algorithm in [24] regards solutions with small constraint violations as feasible ones, the algorithm in [12] regards solutions with constraint violations smaller than a predefined parameter ε as feasible ones, the algorithm in [25] makes no difference when…”
- Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, methodology): “1)Asafuddoula et al. [48] proposed an improved decomposition-based evolutionary algorithm (I-DBEA) for many-objective optimization.”
- A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective Optimization Problems With Large-Scale Variables (2016, methodology): “8) Other interesting issues include analysis of fitness function [54], parameter control [55], locating multiple optimal solutions of nonlinear equation systems [56], using visualization of Pareto-front approximations [57], decomposition [58], two-archive algorithm [59], and reduced objective comput”
- A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, background): “[38] proposed an improved decomposition-based EA, termed I-DBEA, for many-objective optimization.”
- Localized Weighted Sum Method for Many-Objective Optimization (2018, background): “point-driven evolutionary algorithm [23], the improved twoarchive algorithm (Two_Arch2) [24] and [25]–[28].”
- IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, background): “For example, it is difficult to choose the converting strategy of the MaOEAs from the second category, which motivates multiple variants [15], [17], [19], [20], [41], [42] to be developed further.”
- Multifactorial Evolution: Toward Evolutionary Multitasking (2016, background): “mization (MOO) [3]–[7], where every point in the search space maps to a vector-valued objective function.”
- A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, methodology): “To relax the definition of feasibility, the algorithm in [26] regards solutions with small constraint violations as feasible ones; the algorithm in [27] treats two infeasible solutions as nondominated if the angle between them is large; the algorithm in [28] preserves the infeasible solutions in…”
- Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (2017, methodology): “As we have already explained, weight vector-based algorithms in [27]–[30] and [32] have normalization mechanisms.”
- Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization (2017, methodology): “ints equally in case the dierence of their CVs is smaller than a threshold . In particular, this threshold can be adaptively tuned according to the ratio of feasible solutions in the population. In [27], Asafuddoula et al. proposed an adaptive constraint handling method that treats infeasible soluti”
- 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”