Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
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Abstract (arXiv)
In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive evaluation of the MO-MFO algorithms. It is expected that the proposed test problems will germinate progress the field of the MTMOO research.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
4 papers.
- 2023Prompt Evolution for Generative AI: A Classifier-Guided Approachcited by 9
- 2021Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directionscited by 7
- 2025(θl, θu)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spacescited by 4
- 2026Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfercited by 0
- 2026Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfer10 Sep 2026
- 2025(θl, θu)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces11 Mar 2025
- 2023Prompt Evolution for Generative AI: A Classifier-Guided Approach24 May 2023
- 2021Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions4 Feb 2021