Hybrid Metaheuristics Applied to the Multi-objective Spanning Tree Problem
Multi-objective Spanning Tree Problem, Hybrid Metaheuristics, OWA operator, Experimental Algorithms
The Multi-objective Spanning Tree Problem (MSTP) is a NP-hard extension of the Minimum Spanning Tree Problem (MST). Because it models several real-world problems in which conflicting objectives need to be optimized simultaneously, MSTP has been extensively studied in the literature and several exact and heuristic algorithms were proposed. Besides, over the last years, researchers have proved the considerable performance of algorithms that combine various metaheuristics strategies. They are called hybrid algorithms and previous works successfully applied them to several optimization problems. In this work, fve new hybrid algorithms are proposed for two versions of MSTP: three for bi-objective version (BiST) based on Pareto dominance and two for the many-objective version based on Ordered Weighted Average Operator (OWA-ST). This research hybridized concepts from various metaheuristics considering the taxonomy addressed by Talbi (2015). Computational experiments will evaluate the new algorithms with basis in computational time spent and solution quality. The results will be compared to the state-of-the-art. Statistical tests will evaluate the solution quality.