A PSO-inspired architecture to hybridise multi-objective metaheuristics

Abstract

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.