论文标题
对进化算法的单瞄准性双层优化的乐观变体
Optimistic variants of single-objective bilevel optimization for evolutionary algorithms
论文作者
论文摘要
单瞄准性二线优化是约束优化问题的一种专业形式，其中之一是优化问题本身。这些问题通常是非凸面和强烈的np-hard。最近，由于进化计算社区的兴趣越来越高，因为它在现实世界中的应用程序中适用于决策问题，因此建模了二聚体问题。在这项工作中，已经提出了一种局部启发式搜索的部分嵌套进化方法，以解决基准问题并取得出色的结果。这种方法依赖于通婚 - 交叉的概念通过从约束中利用信息来搜索可行区域。还提出了一种新的变体，即常用的收敛方法，即乐观和悲观。它被称为极端乐观的方法。实验结果表明，具有乐观变体的算法与已知的最佳溶液的收敛不同。乐观的方法也表现优于悲观方法。对我们的方法与其他最近发表的部分以完成进化方法的比较统计分析表明了非常竞争的结果。
Single-objective bilevel optimization is a specialized form of constraint optimization problems where one of the constraints is an optimization problem itself. These problems are typically non-convex and strongly NP-Hard. Recently, there has been an increased interest from the evolutionary computation community to model bilevel problems due to its applicability in the real-world applications for decision-making problems. In this work, a partial nested evolutionary approach with a local heuristic search has been proposed to solve the benchmark problems and have outstanding results. This approach relies on the concept of intermarriage-crossover in search of feasible regions by exploiting information from the constraints. A new variant has also been proposed to the commonly used convergence approaches, i.e., optimistic and pessimistic. It is called extreme optimistic approach. The experimental results demonstrate the algorithm converges differently to known optimum solutions with the optimistic variants. Optimistic approach also outperforms pessimistic approach. Comparative statistical analysis of our approach with other recently published partial to complete evolutionary approaches demonstrates very competitive results.
