An easy-to-use real-world multi-objective optimization problem suite

• Published in: Applied soft computing Vol. 89; p. 106078
• Main Authors: Tanabe, Ryoji, Ishibuchi, Hisao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2020
• Subjects: Evolutionary multi-objective optimization; Real-world problems; Test problems; Real-world problems; Evolutionary multi-objective optimization; Test problems
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106078

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.