We propose a decomposition-based interactive evolutionary algorithm (EA) for multiple objective optimization. During an evolutionary search, a decision maker (DM) is asked to compare pairwise solutions from the current population. Using the Monte Carlo simulation, the proposed algorithm generates from a uniform distribution a set of instances of the preference model compatible with such an indirect preference information. These instances are incorporated as the search directions with the aim of systematically converging a population toward the DMs most preferred region of the Pareto front. The experimental comparison proves that the proposed decomposition-based method outperforms the state-of-the-art interactive counterparts of the dominance-based EAs. We also show that the quality of constructed solutions is highly affected by the form of the incorporated preference model.

中文翻译：

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基于分解的多目标优化交互式进化算法

我们提出了一种用于多目标优化的基于分解的交互式进化算法（EA）。在进化搜索过程中，决策者 (DM) 被要求比较当前群体的成对解决方案。使用蒙特卡罗模拟，所提出的算法从均匀分布生成一组与这种间接偏好信息兼容的偏好模型实例。这些实例被合并为搜索方向，目的是系统地将种群收敛到帕累托前沿的 DM 最喜欢的区域。实验比较证明，所提出的基于分解的方法优于基于优势的 EA 的最先进的交互式对应方法。