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Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
lib:a2a6b79f27849cea (v1.0.0)
Authors: Yuan Yuan,Yew-Soon Ong,Liang Feng,A. K. Qin,Abhishek Gupta,Bingshui Da,Qingfu Zhang,Kay Chen Tan,Yaochu Jin,Hisao Ishibuchi
ArXiv: 1706.02766
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1706.02766v1
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.
ArXiv: 1706.02766
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1706.02766v1
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.
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