A Clustering-Based Method for the Multi-Objective Optimization of Combinatorial Problems
Elías D. Niño-Ruiz, Esneyder R. Gonzalez-Ponzon, Jesus D. Pena-Segura, Randy Steven Consuegra-Ortega · 2021
This paper proposes a clustering-based method for Multi-Objective Combinatorial Optimization (MOCO) problems. For a given MOCO, strict variables cluster to approximate a Pareto Front (PF) via a group of intra-clusters solutions. Using a Tabu Search implementation, the PF solutions are enriched. Numerical experiments reveal that, the results obtained by the proposed method are comparable to those of the best metaheuristics from the specialized literature.