Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms
By Zhenan He, Gary G. Yen, Jun Zhang. IEEE Transactions on Evolutionary Computation, 2014
319 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/6242e7cfe1d227b9bcd3bf75dec392ee0fa30c9f
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Citations in the corpus
- A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “CDAS-dominance [26], α-dominance [27], fuzzy Pareto dominance [28], L-dominance [29], θ -dominance [30], and so on [31]–[33] have been experimentally verified to be more”
- A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, methodology): “For instance, the -dominance method [17] employed a relaxed factor to compare the dominance relation between solutions; the preference order approach [18] used a ranking procedure to lower the adverse impact of the Pareto-dominance-based comparison; the fuzzy dominance method [19] was proposed with ”
- Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, methodology): “So far, many alternative preference relations have been proposed for many-objective optimization, such as average and maximum ranking [12], favor relation [13], fuzzy Pareto dominance [14], expansion relation [15], preference order ranking [16], θ -dominance [17], and so on [18]–[20].”
- IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018, methodology): “Furthermore, the fuzzy dominance methods [23], [24] studied the fuzzification of the Pareto-dominance relation to design the ranking scheme to select promising solutions; the L -optimality paradigm was proposed in [25] to pick up solutions whose objectives were with the same importance by…”
- Modified Distance Calculation in Generational Distance and Inverted Generational Distance (2015, methodology): “Among other indicators, the inverted generational distance (IGD [4], [19]) is most frequently used for performance evaluation of EMO algorithms in evolutionary many-objective optimization studies [7], [9], [23].”
- An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, unknown): “k-optimality [35], and fuzzy-based Pareto optimality [36].”
- A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, result): “Other studies along this direction, such as cone -dominance [5], k-optimality [16], preference order ranking [36], fuzzy-dominance [16], [19], θ -dominance [47], and generalized Pareto-optimality [50], have also been shown to provide competitive results.”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “There exist many techniques for developing new dominance relations in the literature, such as expanding the dominance area [6], [7], gridding the objective space [8], [9], adopting the fuzzy logic [10], [11], and defining the dominance relation by weight vectors [12], [13].”