离散多任务协同进化变邻域搜索算法（CoVNS）：在图上社区发现中的应用
A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphs
摘要：
多任务优化范例的主要目标是通过单个搜索过程以同时的方式解决多个并发的优化任务。为了取得有希望的成果，适当利用了各项任务之间的潜在互补性和协同作用，通过交换遗传物质相互帮助。本文的重点是进化多任务，这是一个角度处理多任务优化方案，拥抱概念，从演化计算。这项工作有助于这一领域提出了一种新的多任务的方法命名为协同进化的可变邻域搜索算法，它发现它的灵感都可变邻域搜索元启发式和协同进化策略。本文的第二个贡献是应用领域，这是最优划分的图实例之间的连接是有向和加权。本文对这类任务的同时求解进行了研究。两种不同的多任务的情况下，考虑每个包括11个图形实例。通过我们的方法得到的结果进行比较，由一个并行的可变邻域搜索和独立执行的基本可变邻域搜索。这些结果的讨论支持我们的假设，该方法是一个有前途的计划，同时解决社区发现问题的图。
英文摘要：
The main goal of the multitasking optimization paradigm is to solve multiple and concurrent optimization tasks in a simultaneous way through a single search process. For attaining promising results, potential complementarities and synergies between tasks are properly exploited, helping each other by virtue of the exchange of genetic material. This paper is focused on Evolutionary Multitasking, which is a perspective for dealing with multitasking optimization scenarios by embracing concepts from Evolutionary Computation. This work contributes to this field by presenting a new multitasking approach named as Coevolutionary Variable Neighborhood Search Algorithm, which finds its inspiration on both the Variable Neighborhood Search metaheuristic and coevolutionary strategies. The second contribution of this paper is the application field, which is the optimal partitioning of graph instances whose connections among nodes are directed and weighted. This paper pioneers on the simultaneous solving of this kind of tasks. Two different multitasking scenarios are considered, each comprising 11 graph instances. Results obtained by our method are compared to those issued by a parallel Variable Neighborhood Search and independent executions of the basic Variable Neighborhood Search. The discussion on such results support our hypothesis that the proposed method is a promising scheme for simultaneous solving community detection problems over graphs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
