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Interactive Evolutionary Multiple Objective Optimization for Group Decision Incorporating Value-based Preference Disaggregation Methods

机译：基于价值偏好分解的群体决策交互式进化多目标优化

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摘要

We present a set of interactive evolutionary multiple objective optimization (MOO) methods, called NEMO-GROUP. All proposed approaches incorporate pairwise comparisons of several decision makers (DMs) into the evolutionary search, though evaluating the suitability of solutions for inclusion in the next population in different ways. The performance of algorithms is quantified with various convergence factors derived from the extensive computational tests on a set of benchmark problems. The best individuals and complete populations of solutions constructed by the proposed approaches are evaluated in terms of both utilitarian and egalitarian group value functions for different numbers of DMs. Our results indicate that more promising directions for optimization can be discovered when exploiting the set of value functions compatible with the DMs' preferences rather than selecting a single representative value function for each DM or all DMs considered jointly. We demonstrate that NEMO-GROUP is flexible enough to account for the weights assigned to the DMs. We also show that by appropriately adjusting the elicitation interval and starting generation of the elicitation, one could significantly decrease the number of pairwise comparisons the DMs need to perform to construct a satisfactory solution.
机译：我们提出了一套称为NEMO-GROUP的交互式进化多目标优化（MOO）方法。所有提出的方法都将几个决策者（DM）的成对比较纳入进化搜索，尽管以不同的方式评估了解决方案是否适合纳入下一种群。通过对一系列基准问题进行广泛的计算测试得出的各种收敛因子，可以量化算法的性能。根据不同数量的DM的功利和均等群体价值函数，评估了所提出方法构建的最佳个体和完整解决方案群体。我们的结果表明，当利用与DM的偏好兼容的价值函数集而不是为每个DM或联合考虑的所有DM选择单个代表性的价值函数时，可以发现更有希望的优化方向。我们证明NEMO-GROUP足够灵活，可以解决分配给DM的权重。我们还表明，通过适当地调整激发间隔并开始激发的产生，可以显着减少DM需要执行以构建令人满意的解决方案的成对比较的次数。

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