An Archive-Based Multi-Objective Simulated Annealing Algorithm for the Time/Weight-Balanced Cluster Problem in Delivery Logistics

This paper introduces an archive-based multi- objective algorithm based on simulated annealing to deal with the time/weight-balanced cluster problem. In the presented paper, we adapted the necessary components into the Archive Multi- Objective Simulated Annealing (AMOSA) framework to deal appropriat...

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Veröffentlicht in:2023 IEEE Congress on Evolutionary Computation (CEC) S. 1 - 8
Hauptverfasser: Ceja-Cruz, Eduardo Manuel, Menchaca-Mendez, Adriana, Montero, Elizabeth, Zapotecas-Martinez, Saul
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2023
Schlagworte:
Clustering algorithms
clustering problem
Computational efficiency
Evolutionary computation
Linear programming
Logistics
multi-objective optimization
Simulated annealing
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Zusammenfassung:This paper introduces an archive-based multi- objective algorithm based on simulated annealing to deal with the time/weight-balanced cluster problem. In the presented paper, we adapted the necessary components into the Archive Multi- Objective Simulated Annealing (AMOSA) framework to deal appropriately with the clustering problem. Due to the computational cost of the objective functions, we introduced a parallelization of such objectives to reduce the algorithm's running time, achieving an improvement of 13%. The introduced algorithm was evaluated in a real-world scenario, and its parameters were tuned using the Iterated Local Search in Parameter Configuration Space (ParamILS) method. AMOSA, using its best configuration, was compared concerning a popular Pareto-based multi-objective evolutionary algorithm. The preliminary results indicate the viability of using the proposed approach to deal with the type of problem tackled in this study. The proposed method outperformed the Nondominated Sorting Genetic Algorithm II (NSGA II) concerning the quality of the solutions and the execution time, as will be seen later on.
DOI:10.1109/CEC53210.2023.10253992