Hybridisation is a technique that exploits and unites the best features of individual algorithms. The literature includes several hybridisation methodologies, among which there are general procedures, termed architectures, that provide generic functionalities and features for solving optimisation problems. Successful hybridisation methodologies have applied concepts of the multi-agent paradigm, such as cooperation and agent intelligence. However, there is still a lack concerning architectures for the hybridisation of multi-objective metaheuristics that fully explore these concepts. This study proposes a new architecture, named MO-MAHM, based on concepts from Particle Swarm Optimisation, to hybridise multi-objective metaheuristics. We apply the MO-MAHM to the Bi-objective Spanning Tree Problem. Four algorithms were hybridised within the MO-MAHM: three evolutionary algorithms and a local search method. We report the results of experiments with 180 instances, analyse the behaviour of the MO-MAHM, and compare to the results produced by algorithms proposed for the Bi-objective Spanning Tree Problem.

中文翻译：

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受PSO启发的体系结构，用于混合多目标元启发式方法

混合是一种利用并统一单个算法的最佳功能的技术。文献包括几种杂交方法，其中有一些通用的过程称为体系结构，它们为解决优化问题提供了通用的功能和特征。成功的杂交方法论已经应用了多主体范例的概念，例如合作和主体智能。然而，仍然缺乏关于充分探索这些概念的用于多目标元启发式方法混合的体系结构。这项研究基于粒子群优化的概念，提出了一种名为MO-MAHM的新架构，以混合多目标元启发式方法。我们应用MO-MAHM双目标生成树问题。MO-MAHM中混合了四种算法：三种进化算法和局部搜索方法。我们报告了180个实例的实验结果，分析了MO-MAHM的行为，并与针对双目标生成树问题提出的算法所产生的结果进行了比较。