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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Published in:2023 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors: Ceja-Cruz, Eduardo Manuel, Menchaca-Mendez, Adriana, Montero, Elizabeth, Zapotecas-Martinez, Saul
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2023
Subjects:
Clustering algorithms
clustering problem
Computational efficiency
Evolutionary computation
Linear programming
Logistics
multi-objective optimization
Simulated annealing
Sorting
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Abstract 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.
AbstractList 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.
Author Montero, Elizabeth
Zapotecas-Martinez, Saul
Ceja-Cruz, Eduardo Manuel
Menchaca-Mendez, Adriana
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  givenname: Eduardo Manuel
  surname: Ceja-Cruz
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  givenname: Adriana
  surname: Menchaca-Mendez
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  organization: UNAM Morelia,Technologies for Information in Sciences ENES Unidad Morelia,Michoacán,Mexico
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  givenname: Elizabeth
  surname: Montero
  fullname: Montero, Elizabeth
  email: elizabeth.montero@usm.cl
  organization: Universidad Técnica Federico Santa Mariá,Departamento de Informática,Valparaiso,Chile
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  givenname: Saul
  surname: Zapotecas-Martinez
  fullname: Zapotecas-Martinez, Saul
  email: szapotecas@inaoep.mx
  organization: Instituto Nacional de Astrofisica Óptica y Electrónica Tonantzintla,Computer Science Department,Puebla,Mexico
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Snippet This paper introduces an archive-based multi- objective algorithm based on simulated annealing to deal with the time/weight-balanced cluster problem. In the...
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SubjectTerms Clustering algorithms
clustering problem
Computational efficiency
Evolutionary computation
Linear programming
Logistics
multi-objective optimization
Simulated annealing
Sorting
Title An Archive-Based Multi-Objective Simulated Annealing Algorithm for the Time/Weight-Balanced Cluster Problem in Delivery Logistics
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