Enhancing Grey Wolf Optimization Using Preprocessing Techniques to Solve the Travelling Salesman Problem with Time Windows

Archana A. Deshpande; Seema Raut; Nalini V. Vaidya

doi:10.48084/etasr.12852

Authors

Archana A. Deshpande G H Raisoni University, Amravati, Maharashtra, India
Seema Raut Applied Mathematics Department, G H Raisoni University, Amravati, Maharashtra, India
Nalini V. Vaidya Applied Mathematics Department, G H Raisoni College of Engineering, Nagpur, Maharashtra, India

Volume: 16 | Issue: 2 | Pages: 32913-32920 | April 2026 | https://doi.org/10.48084/etasr.12852

Received: 21 June 2025 | Revised: 31 July 2025, 22 August 2025, and 24 September 2025 | Accepted: 27 September 2025 | Online: 17 February 2026

Corresponding author: Archana A. Deshpande

Abstract

The aim of the NP-hard Traveling Salesperson Problem with Time Windows (TSPTW) is to visit a predetermined customer group within the time frames allotted to them while minimizing a predetermined objective function. This problem considers a salesman who leaves his house, has to go to several places in a given amount of time, and then returns. The Gray Wolf Optimizer (GWO) is a bioinspired meta-heuristic population-based algorithm that mimics the survival strategies of gray wolves. This study applies the GWO strategy to minimize travel expenses within the allotted period, incorporating preprocessing to improve performance. The effectiveness of the proposed method is evaluated using reputable benchmark cases to reduce overall travel expenses. The MATLAB environment was used to implement the GWO. Based on the computational results, GWO performs much better than other similar algorithms.

Keywords:

traveling salesman problem with time window, grey wolf optimization, preprocessing

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