论文标题
易于使用的现实世界多目标优化问题套件
An Easy-to-use Real-world Multi-objective Optimization Problem Suite
论文作者
论文摘要
尽管合成测试问题被广泛用于进化多目标优化算法的性能评估，但它们可能包括可能导致高估/低估的不现实特性。为了解决这个问题，我们提出了一个由16个约束现实世界中的问题组成的多目标优化问题套件。问题套件在目标数量，帕累托前部的形状和设计变量的类型方面包括各种问题。在16个问题中，有4个是多目标混合构成优化问题。我们提供16个问题的Java，C和MATLAB源代码，以便以现成的方式使用它们。我们检查了每个测试问题的近似帕累托正面。我们还分析了16个问题上六种代表性进化多目标优化算法的性能。除了16个问题外，我们还提出了8个受约束的多目标现实世界问题。
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.
