In the field of complex problem optimization with metaheuristics, semantics has been used for modeling different aspects, such as: problem characterization, parameters, decision-maker’s preferences, or algorithms. However, there is a lack of approaches where ontologies are applied in a direct way into the optimization process, with the aim of enhancing it by allowing the systematic incorporation of additional domain knowledge. This is due to the high level of abstraction of ontologies, which makes them difficult to be mapped into the code implementing the problems and/or the specific operators of metaheuristics. In this paper, we present a strategy to inject domain knowledge (by reusing existing ontologies or creating a new one) into a problem implementation that will be optimized using a metaheuristic. Thus, this approach based on accepted ontologies enables building and exploiting complex computing systems in optimization problems. We describe a methodology to automatically induce user choices (taken from the ontology) into the problem implementations provided by the jMetal optimization framework. With the aim of illustrating our proposal, we focus on the urban domain. Concretely, we start from defining an ontology representing the domain semantics for a city (e.g., building, bridges, point of interest, routes, etc.) that allows defining a-priori preferences by a decision maker in a standard, reusable, and formal (logic-based) way. We validate our proposal with several instances of two use cases, consisting in bi-objective formulations of the Traveling Salesman Problem (TSP) and the Radio Network Design problem (RND), both in the context of an urban scenario. The results of the experiments conducted show how the semantic specification of domain constraints are effectively mapped into feasible solutions of the tackled TSP and RND scenarios. This proposal aims at representing a step forward towards the automatic modeling and adaptation of optimization problems guided by semantics, where the annotation of a human expert can be now considered during the optimization process.

在具有元启发式算法的复杂问题优化领域，语义已用于对不同方面进行建模，例如：问题特征，参数，决策者的偏好或算法。然而，缺乏将本体直接应用于优化过程的方法，目的是通过允许系统地结合额外的领域知识来增强它。这是由于本体的高度抽象，这使得它们难以映射到实现问题的代码和/或元启发式的特定操作符中。在本文中，我们提出了一种将领域知识（通过重用现有本体或创建新本体）注入将使用元启发式优化的问题实现的策略。因此，这种基于公认本体的方法能够在优化问题中构建和利用复杂的计算系统。我们描述了一种自动将用户选择（取自本体）引入 jMetal 优化框架提供的问题实现的方法。为了说明我们的建议，我们专注于城市领域。具体来说，我们从定义一个代表城市领域语义的本体开始（例如，建筑物、桥梁、兴趣点、路线等），它允许决策者以标准、可重用和正式的方式定义先验偏好。 （基于逻辑的）方式。我们通过两个用例的几个实例来验证我们的提案，其中包括旅行销售员问题（TSP）和无线电网络设计问题（RND）的双目标表述，无论是在城市场景的背景下。所进行的实验结果表明，域约束的语义规范如何有效地映射到解决的 TSP 和 RND 场景的可行解决方案中。该提议旨在代表朝着语义引导的优化问题的自动建模和适应迈进了一步，现在可以在优化过程中考虑人类专家的注释。