Multiproblem Surrogates: Transfer Evolutionary Multiobjective Optimization of Computationally Expensive Problems
提出一种自适应知识复用框架，利用多问题代理在相关优化问题间迁移学习模型，以高效求解计算昂贵的多目标优化问题，在合成基准和实际案例中验证了有效性。
In most real-world settings, designs are often gradually adapted and improved over time. Consequently, there exists knowledge from distinct (but possibly related) design exercises, which have either been previously completed or are currently in-progress, that may be leveraged to enhance the optimization performance of a particular target optimization task of interest. Further, it is observed that modern day design cycles are typically distributed in nature, and consist of multiple teams working on associated ideas in tandem. In such environments, vast amounts of related information can become available at various stages of the search process corresponding to some ongoing target optimization exercise. Successfully exploiting this knowledge is expected to be of significant value in many practical settings, where solving an optimization problem from scratch may be exorbitantly costly or time consuming. Accordingly, in this paper, we propose an adaptive knowledge reuse framework for surrogate-assisted multiobjective optimization of computationally expensive problems, based on the novel idea of multiproblem surrogates. This idea provides the capability to acquire and spontaneously transfer learned models across problems, facilitating efficient global optimization. The efficacy of our proposition is demonstrated on a series of synthetic benchmark functions, as well as two practical case studies.