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Conceptual Framework for Adaptive Bacterial Memetic Algorithm Parameterization in Storage Location Assignment Problem

Author

Listed:
  • Kitti Udvardy

    (Multidisciplinary Doctoral School of Engineering, Széchenyi University, 9026 Győr, Hungary)

  • Polina Görbe

    (Department of Logistics and Forwarding, Széchenyi University, 9026 Győr, Hungary)

  • Tamás Bódis

    (Department of Logistics and Forwarding, Széchenyi University, 9026 Győr, Hungary)

  • János Botzheim

    (Department of Artificial Intelligence, Faculty of Informatics, ELTE Eötvös Loránd University, 1117 Budapest, Hungary)

Abstract

Recognized as an NP-hard combinatorial challenge, Storage Location Assignment Problem (SLAP) demands heuristic or algorithmic solutions for effective optimization. This paper specifically examines the enhancement of SLAP through the utilization of evolutionary algorithms, as they are particularly suitable for complex cases. Among others, the genetic algorithm (GA) is typically applied to solve this problem. This paper investigates the Bacterial Memetic Algorithm (BMA) as a possible solution for optimization. Though the comparative analysis of the BMA with the previously well-used GA algorithm under certain test parameters reveals that BMA is suitable for SLA optimization, BMA failed to achieve better results. We attribute the unsatisfactory results to the parameter settings, as illustrated by a few specific examples. However, the complexity of the problem and the parameterization does not allow for continuous manual parameter adjustment, which is why we have identified the need for a concept that automatically and adaptively adjusts the parameter settings based on the statistics and fitness values obtained during the execution. The novelty of this paper is to specify the concept of adaptive BMA parameterization and rules.

Suggested Citation

  • Kitti Udvardy & Polina Görbe & Tamás Bódis & János Botzheim, 2024. "Conceptual Framework for Adaptive Bacterial Memetic Algorithm Parameterization in Storage Location Assignment Problem," Mathematics, MDPI, vol. 12(23), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3688-:d:1528800
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    References listed on IDEAS

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    6. Yuyan He & Aihu Wang & Hailiang Su & Mengyao Wang, 2019. "Particle Swarm Optimization Using Neighborhood-Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-13, July.
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