Combining Convergence and Diversity in Evolutionary Multiobjective Optimization

By Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, Eckart Zitzler. Evolutionary Computation, 2002

1,584 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/5dc852be7d9a28d29e5a3d2d32f63efee61e3e2d

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Citations in the corpus

A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, background): “[30] to combine both convergence and diversity of solutions in a compact form.”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, background): “To enhance the convergence pressure, the most intuitive idea is to directly modify the definition of traditional Pareto dominance, such as ε-dominance [12], [13], L-optimality [14], fuzzy dominance [15], preference order ranking [16], and θ -dominance [17].”
Reference point based multi-objective optimization using evolutionary algorithms (2006, methodology): “Moreover, the use of the -based selection strategy (which is also similar to the -dominance strategies suggested elsewhere [17, 18]) ensures a spread of solutions near the preferred Pareto-optimal regions.”
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 Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, background): “The εdominance [24], CDAS-dominance [25], α-dominance [26], fuzzy Pareto dominance [27], L-dominance [28], θ-dominance [29], and so on [30]–[32] have been experimentally verified to be more effective than the original Pareto dominance relation when searching toward the optimum.”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “Furthermore, the ε-dominance concept [182] was adopted to update an external archive in which the non-dominated solutions found during the search were stored.”
An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization (2015, methodology): “-dominance [15] is used in comparison or when the popu-”
Quality and Diversity Optimization: A Unifying Modular Framework (2017, background): “-dominance [41], that we name the exclusive -dominance.”
Evaluating the -Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions (2005, methodology): “To obtain a better spread, the suggestions of other -dominance criteria outlined elsewhere (Laumanns et al., 2002) can be implemented to the -MOEA.”
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, neutral): “This type of ideas has been widely adopted for solving MaOPs, such as -dominance [22], [23], L-optimality [24], fuzzy dominance [25], and preference order ranking [26].”
A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization (2016, background): “Up to now, many alternative preference relations, such as favor relation [15], dominance [16], [17], fuzzy Pareto dominance [18], [19], preference order”