A fuzzy definition of "optimality" for many-criteria optimization problems

By Marco Farina, Paolo Amato. IEEE Trans. Syst. Man Cybern. Part A, 2004

335 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ae6b08e64d09cfa29a4cbb3af1348d752e6043c9

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In Computational Theory and Mathematics

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Citations in the corpus

A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization (2009, background): “There have been significant contributions made to the field of evolutionary multiobjective optimization (EMOO) in the past two decades, as a result of intense research examining topics such as fitness assignment [15], [16], diversity preservation [13], [35], and elitism [3], [39], [57].”
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, background): “Farina and Amato (2004), Fleming et al. (2005), and Purshouse and Fleming (2007) observe that the proportion of locally Pareto non-dominated solutions tends to become large as the number of objectives increases.”
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization (2013, background): “trol [6], [59], k-optimality [18], preference order ranking [17], subspace dominance comparison [2], [35], -dominance [46], and fuzzy Pareto dominance [43].”
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction (2011, methodology): “Another EMO study used a fuzzy dominance [58] relation (instead of Paretodominance), in which superiority of one solution over another in any objective is defined in a fuzzy manner.”
An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition (2015, neutral): “k-optimality [35], and fuzzy-based Pareto optimality [36].”
Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms (2014, methodology): “Farina and Amato [15] designed the fuzzy-based definitions of optimality and domination for human decision making in many-objective optimization problems.”
Bio-inspired computation: Where we stand and what's next (2019, background): “For instance, the number of non-dominated solutions is known to grow exponentially with the number of objectives [135], which makes the selection mechanism in Pareto-based methods completely useless since all the non-dominated solutions are considered equally good.”
A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “1) Dominance Relations: The proposed SDR is compared with Pareto dominance [41], CDAS [6], S-CDAS [24], GPO [7], grid dominance [9], (1−k)-dominance [28], L-dominance [29], and θ -dominance [12] in the experiments.”
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.”