An Easy-to-use Real-world Multi-objective Optimization Problem Suite
- URL: http://arxiv.org/abs/2009.12867v1
- Date: Sun, 27 Sep 2020 15:11:08 GMT
- Title: An Easy-to-use Real-world Multi-objective Optimization Problem Suite
- Authors: Ryoji Tanabe and Hisao Ishibuchi
- Abstract summary: We present a multi-objective optimization problem suite consisting of 16 bound-constrained real-world problems.
4 out of the 16 problems are multi-objective mixed-integer optimization problems.
We analyze the performance of six representative evolutionary multi-objective optimization algorithms on the 16 problems.
- Score: 7.81768535871051
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although synthetic test problems are widely used for the performance
assessment of evolutionary multi-objective optimization algorithms, they are
likely to include unrealistic properties which may lead to
overestimation/underestimation. To address this issue, we present a
multi-objective optimization problem suite consisting of 16 bound-constrained
real-world problems. The problem suite includes various problems in terms of
the number of objectives, the shape of the Pareto front, and the type of design
variables. 4 out of the 16 problems are multi-objective mixed-integer
optimization problems. We provide Java, C, and Matlab source codes of the 16
problems so that they are available in an off-the-shelf manner. We examine an
approximated Pareto front of each test problem. We also analyze the performance
of six representative evolutionary multi-objective optimization algorithms on
the 16 problems. In addition to the 16 problems, we present 8 constrained
multi-objective real-world problems.
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