Scalable Test Problems for Evolutionary Multiobjective Optimization

By Kalyanmoy Deb, Lothar Thiele, Marco Laumanns, Eckart Zitzler. Evolutionary Multiobjective Optimization, 2005

2,288 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/277706e9ea2a0aea2d7433089fee5e163205dc4a

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Citations in the corpus

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making (2018, background): “We hope that this work inspires new real-world applications involving multiple objectives, and will be used in multi-objective optimisation [15, 16, 23, 29], planning [4, 41, 48, 53], and RL [2, 33, 42, 52] alike.”
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, methodology): “Among many others, the ZDT test suite [20] and DTLZ test suite [21], [22] are the most widely used ones.”
The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration (2007, background): “These difficulties become more serious as the number of objectives in the problem formulation increases, see [3] for details.”
HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization (2011, positive): “HypE was compared to various state-of-the-art MOEAs with regard to the hy-pervolume indicator values of the generated Pareto-set approximations—on the DTLZ (Deb et al., 2005), the WFG (Huband et al., 2006), and the knapsack (Zitzler and Thiele, 1999) testproblem suites.”
jMetal: A Java framework for multi-objective optimization (2011, positive): “A rich set of test problems including: – Problem families: Zitzler–Deb–Thiele (ZDT) [26], Deb– Thiele–Laumanns–Zitzler (DTLZ) [27], Walking-Fish-Group (WFG) test problems [28]), CEC2009 (unconstrained problems) [29], and the Li-Zhang benchmark [17].”
A Scalable Multi-objective Test Problem Toolkit (2005, methodology): “There have been several attempts to define test suites and toolkits for testing multi-objective evolutionary algorithms (MOEAs) [1, 2, 3, 4].”
An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, methodology): “…on testing the performance of algorithms in ﬁnding sparse Pareto optimal solutions, three simple Pareto fronts (i.e., sets of shape functions) taken from DTLZ [58] and WFG [60] are adopted in SMOP1–SMOP8, namely, the linear Pareto front, the convex Pareto front, and the concave Pareto front.”
A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization (2017, methodology): “The test problems used here are the MOP [34] test suite, which is a modification of ZDT [52] and DTLZ [15] but more difficult than its predecessors.”
A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “4) Benchmark Problems: The widely used DTLZ [47] and WFG [48] test suites are employed as the test”
A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “Based on the DTLZ test problems [143], eight equality constrained optimization problems were proposed in the Eq-DTLZ test suite [142].”
Stable Matching-Based Selection in Evolutionary Multiobjective Optimization (2014, background): “The total number of function evaluations is 20 000 for the bi-objective test instances, and 30 000 for the three-objective ones except DTLZ3, where 100 000 function evaluations are used due to its difficulties.”
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (2014, methodology): “To start, we use 3- to 15-objective DTLZ1, DTLZ2, DTLZ3, and DTLZ4 problems [53].”