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According to the published papers and books since the turn of the century,Pareto optimization is the dominating assessment method for multi-objectivenonlinear optimization problems treated by population-based optimizers likeEvolutionary Algorithms. However, is it always the method of choice forreal-world applications, where either more than four objectives have to beconsidered, or the same type of task is repeated again and again with onlyminor modifications, in an automated optimization or planning process? Thispaper presents a classification of application scenarios and compares thePareto approach with an extended version of the weighted sum, called cascadedweighted sum, for the different scenarios. Its range of application within thefield of multi-objective optimization is discussed as well as its strengths andweaknesses.

Pareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts