In many practical situations the decision-maker has to pay special attention to decision space to determine the constructability of a potential solution, in addition to its optimality in objective space. Practically desirable solutions are those around preferred values in decision space and within a distance from optimality. This work investigates two methods to find simultaneously optimal and practically desirable solutions. The methods expand the objective space by adding fitness functions that favor preferred values for some variables. In addition, the methods incorporate a ranking mechanism that takes into account Pareto dominance in objective space and desirability in decision space. One method searches with one population in the expanded space, whereas the other one uses two populations to search concurrently in the original and expanded space. Our experimental results on benchmark and real world problems show that the proposed method can effectively find optimal and practically desirable solutions.

中文翻译：

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渐进多目标优化，包括实际需要的解决方案

在许多实际情况下，决策者除了要在目标空间中实现最优之外，还必须特别注意决策空间，以确定潜在解决方案的可构造性。实际理想的解决方案是决策空间中的最佳值附近以及距最佳状态不远的解决方案。这项工作研究两种方法来同时找到最佳和实际理想的解决方案。该方法通过添加适合某些变量优选值的适应度函数来扩展目标空间。另外，这些方法结合了一种排序机制，该机制考虑了目标空间中帕累托优势和决策空间中的合意性。一种方法是在扩展空间中搜索一个种群，而另一个使用两个总体在原始空间和扩展空间中同时搜索。我们在基准问题和实际问题上的实验结果表明，所提出的方法可以有效地找到最佳的和实际理想的解决方案。