An easy-to-use real-world multi-objective optimization problem suite
By Ryoji Tanabe, Hisao Ishibuchi. Applied Soft Computing, 2020
329 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/ad8c95627ebb85fde2026bf2ecfe7b4d980983d6
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Citations in the corpus
- Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization (2020, methodology): “For this problem, we optimize the surrogate from Tanabe and Ishibuchi [57].”
- A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “In [91] an easy-to-use multi-objective optimization suite is introduced, consisting of 16 bound-constrained real-world problems.”