Applying a Hybrid Gray Wolf‐Enhanced Whale Optimization Algorithm to the Capacitated Vehicle Routing Problem

The study presents a novel hybrid gray wolf and whale optimization algorithm (hGWOAM) for the capacitated vehicle routing problem (CVRP). By integrating the enhanced whale optimization algorithm (EWOA) and gray wolf optimizer (GWO) with tournament selection, opposition‐based learning, and mutation t...

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Bibliographic Details
Published in:Journal of advanced transportation Vol. 2025; no. 1
Main Authors: Pham, Vu Hong Son, Nguyen, Van Nam, Nguyen Dang, Nghiep Trinh
Format: Journal Article
Language:English
Published: London John Wiley & Sons, Inc 01.01.2025
Wiley
Subjects:
Algorithms
Behavior
Canis lupus
Case studies
Comparative analysis
Deviation
Energy efficiency
Environmental impact
Exploitation
Heuristic methods
Linear programming
Localization
Logistics
Marine mammals
Mathematical optimization
Mutation
Optimization
Optimization algorithms
Supply chains
Unmanned aerial vehicles
Vehicle routing
Vehicles
Whales & whaling
ISSN:0197-6729, 2042-3195
Online Access:Get full text
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Summary:The study presents a novel hybrid gray wolf and whale optimization algorithm (hGWOAM) for the capacitated vehicle routing problem (CVRP). By integrating the enhanced whale optimization algorithm (EWOA) and gray wolf optimizer (GWO) with tournament selection, opposition‐based learning, and mutation techniques, hGWOAM enhances routing efficiency under capacity constraints. Computational evaluations demonstrate its superior performance, achieving lower percentage deviations (%dev) compared to existing algorithms across multiple case studies and real‐world applications. In Case Study 1, hGWOAM achieved a mean percentage deviation (%dev) lower than EWOA (0.89%), GWO (0.74%), SCA (0.59%), DA (1.63%), ALO (2.26%), MHPSO (1.85%), PSO (1.96%), DPGA (2.85%), and SGA (4.14%). In Case Study 2, hGWOAM outperformed EWOA (12.05%), GWO (2.53%), ALO (21.07%), and DA (17.58%). In a real‐world application, it achieved the best %dev, surpassing EWOA (6.64%), GWO (6.34%), ALO (9.01%), and DA (12.24%). These findings highlight hGWOAM’s potential for optimizing logistics, reducing operational costs, and minimizing environmental impact while also paving the way for future advancements in metaheuristic optimization.
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ISSN:0197-6729
2042-3195
DOI:10.1155/atr/5584617