Interactive metaheuristics for optimization-based decision support systems

本研究旨在通过在交互环境中考虑这些优化方法来支持元启发式在实际应用中的集成。尽管元启发式已被证明在各种应用领域中作为真正的问题解决者是成功的，但一些限制限制了元启发式在决策支持系统中的使用。首先，在不了解实际情况的情况下，很难设置一些策略参数。其次，问题模型可能低估或忽略问题的某些方面。最后，决策支持系统的用户可能不信任元启发式提供的解决方案。在决策支持系统中实际使用元启发式的这些障碍可以通过在解决过程中利用人类专业知识来克服。在此背景下，本研究计划将探讨互动元启发式。在这种原始的元启发式方法中，与用户的交互旨在指导搜索过程，丰富问题模型，涉及并告知用户优化过程所采取的决策。该项目的主要目标是为决策支持系统的交互式元启发式设计开发新的模型、算法和软件解决方案。

This research intends to support the integration of metaheuristics in real applications by considering these optimization methods in an interactive context. In spite of the fact that metaheuristics have been proven to be successful as real problem solvers in a variety of application areas, several limitations restrain the use of metaheuristics in decision support systems. First, some strategic parameters are difficult to set without knowledge about real instances to solve. Second, the problem model may underestimate or ignore some aspect of the problem. Finally, the user of the decision support system may distrust the solutions provided by the metaheuristic. These obstacles to a practical use of metaheuristics in decision support systems may be overcome by taking advantage of human expertise during the solving process. In this context, the research project will investigate interactive metaheuristics. In this original approach of metaheuristics, the interaction with the user aims at guiding the search process, enriching the problem model, involving and informing the user on the decisions taken by the optimization process. The main goal of the project is to develop new models, algorithms and software solutions for the design of interactive metaheuristics for decision support systems.