An Easy-to-use Real-world Multi-objective Optimization Problem Suite

TL;DR

This paper introduces a new suite of 16 real-world multi-objective optimization problems, along with source codes, to provide more realistic benchmarks for evaluating evolutionary algorithms.

Contribution

It presents a diverse, easy-to-use problem suite with source codes, addressing limitations of synthetic test problems in multi-objective optimization research.

Findings

Analyzed Pareto fronts of each problem.

Evaluated six evolutionary algorithms on the suite.

Provided additional constrained real-world problems.

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.