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A modified coronavirus herd immunity optimizer for capacitated vehicle routing problem.

Lamees Mohammad Dalbah1, Mohammed Azmi Al-Betar1,2, Mohammed A Awadallah3,4

  • 1Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.

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A modified Coronavirus Herd Immunity Optimizer (CHIO) effectively solves the capacitated vehicle routing problem (CVRP). This metaheuristic algorithm demonstrates competitive results on complex datasets, offering efficient routing solutions.

Keywords:
COVID-19Coronavirus Herd Immunity Optimizer (CHIO)MetaheuristicsOptimizationVehicle routing problem

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Area of Science:

  • Operations Research
  • Computer Science
  • Optimization Algorithms

Background:

  • The Capacitated Vehicle Routing Problem (CVRP) is an NP-hard optimization challenge.
  • Metaheuristic algorithms are commonly employed to address the complexity of CVRP.
  • The Coronavirus Herd Immunity Optimizer (CHIO) is a novel population-based metaheuristic algorithm.

Purpose of the Study:

  • To modify the CHIO algorithm for efficient solution of the CVRP.
  • To evaluate the performance of the modified CHIO on benchmark CVRP datasets.
  • To compare the modified CHIO against existing state-of-the-art algorithms.

Main Methods:

  • Modification of CHIO operators to ensure solution feasibility for CVRP.
  • Testing the modified CHIO on synthetic CVRP models and the ABEFMP dataset.
  • Comparative analysis of modified CHIO results with 13 other algorithms.

Main Results:

  • Modified CHIO achieved comparable results on 2 out of 10 synthetic instances.
  • The algorithm provided acceptable results for the remaining synthetic instances.
  • On the more complex ABEFMP dataset, modified CHIO achieved highly competitive results, ranking first in 8 out of 27 instances.

Conclusions:

  • The modified CHIO algorithm is an efficient method for solving the CVRP.
  • This approach shows promise for application to other vehicle routing problems, including the multiple traveling salesman problem.