A Scalable Multi-objective Test Problem Toolkit

By Simon Huband, L. Barone, Lyndon While, Philip Hingston. International Conference on Evolutionary Multi-Criterion Optimization, 2005

397 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/14ee6fa596cfcc73f87669ec79fc47ceaf13b937

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Citations in the corpus

A review of multiobjective test problems and a scalable test problem toolkit (2006, methodology): “…parameters (the ﬁrst and the last working parametersareposition-anddistance-relatedparameters,respectively), isadistancescalingconstant(equaltoonein[43]), aredegeneracyconstants(foreach , the dimensionality of the Pareto optimal front is reduced by one), are shape functions, are scaling constants,…”
Direct Multisearch for Multiobjective Optimization (2011, neutral): “Problem n m Problem n m Problem n m BK1 [25] 2 2 I5 [24] 8 3 MOP3 [25] 2 2 CL1 [6] 4 2 IKK1 [25] 2 3 MOP4 [25] 3 2 Deb41 [15] 2 2 IM1 [25] 2 2 MOP5 [25] 2 3 Deb512a [15] 2 2 Jin1 [48] 2 2 MOP6 [25] 2 2 Deb512b [15] 2 2 Jin2 [48] 2 2 MOP7 [25] 2 3 Deb512c [15] 2 2 Jin3 [48] 2 2 OKA1 [39] 2 2 Deb513 [”
Bio-inspired computation: Where we stand and what's next (2019, background): “A common practice of current research in multi-/many-objective optimization is to propose new algorithmic variants based on existing benchmark functions (e.g. ZDT, DTLZ, WFG, UF), or adapt bioinspired solvers to accommodate multi-objectivity.”
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization (2016, methodology): “The other nine test problems are WFG1 to WFG9 taken from the WFG test suite [71], [75], which are designed by introducing difficulties in both the decision space (e.”
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization (2015, methodology): “The WFG test suite was first introduced in [59] and systematically reviewed and analyzed in [55], which was designed with the aim to introduce a class of difficult benchmark problems for evaluating the performance of MOEAs.”
Benchmark Problems and Performance Indicators for Search of Knee Points in Multiobjective Optimization (2020, methodology): “The design of the WFG problems [48], [49] also follows the bottom-up approach [47] but differs from the approach to designing the ZDT and DTLZ test suites.”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, background): “WFG4 from the WFG test set [52], which are with multimodal objective functions and nonseparable variables.”
AbYSS: Adapting Scatter Search to Multiobjective Optimization (2008, methodology): “We also include in this set the bi-objective version of the nine problems, WFG1 to WFG9, defined in [26] using the WFG Toolkit.”
A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization (2018, methodology): “To more convincingly demonstrate the performance of K-RVEA, we tested and compared the algorithm on the WFG problems [22].”