  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.
