帕累托最优或级联加权和：概念的比较
Pareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts
摘要：
根据本世纪以来发表的论文和书籍，Pareto优化是基于种群的优化算法（如进化算法）处理多目标非线性优化问题的主要评估方法。然而，它是否总是现实世界应用的首选方法，其中必须考虑四个以上的目标，或者在自动优化或规划过程中仅进行微小修改就重复相同类型的任务？本文提出了一种应用场景的分类，并比较了帕累托方法与扩展版本的加权和，称为级联加权和，为不同的情况。它的应用范围内的多目标优化领域进行了讨论，以及它的优点和缺点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
