论文标题
帕累托优化或级联加权总和：概念的比较
Pareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts
论文作者
论文摘要
根据本世纪之交以来发表的论文和书籍，帕累托优化是多目标非线性优化问题的主导评估方法，这些问题由基于人群的优化者（例如进化算法）处理。但是，在自动化的优化或计划过程中，必须一次又一次地重复一次又一次的修改？本文介绍了应用程序方案的分类，并将帕累托方法与用于不同方案的加权总和的扩展版本（称为级联加权总和）进行了比较。讨论了其在多目标优化领域的应用范围及其优势和劣势。
According to the published papers and books since the turn of the century, Pareto optimization is the dominating assessment method for multi-objective nonlinear optimization problems treated by population-based optimizers like Evolutionary Algorithms. However, is it always the method of choice for real-world applications, where either more than four objectives have to be considered, or the same type of task is repeated again and again with only minor modifications, in an automated optimization or planning process? This paper presents a classification of application scenarios and compares the Pareto approach with an extended version of the weighted sum, called cascaded weighted sum, for the different scenarios. Its range of application within the field of multi-objective optimization is discussed as well as its strengths and weaknesses.
