An Enhanced Red Deer Algorithm with Adaptive Memory Strategy for the Capacitated Vehicle Routing Problem
DOI:
https://doi.org/10.32890/jict2026.25.2.4Keywords:
Vehicle routing problem, metaheuristics, Red Deer algorithm, exploration and exploitation, memory structureAbstract
The vehicle routing problem (VRP), especially its capacitated (CVRP), has been extensively studied. In the CVRP, each customer must be served exactly once without exceeding the vehicle’s capacity, with the aim of minimising the total distance of all routes. As a nondeterministic polynomial (NP)-hard problem, the CVRP is best solved using metaheuristics. The red deer algorithm (RDA), inspired by red deer mating behaviour, is population-based search metaheuristic with both exploration and exploitation phases. However, it may still get trapped in local optima and lacks a strong intensification mechanism for refining solutions. In RDA, not all solutions are explored, as it imposes no limit on solution reproduction, thereby restricting exploration. Thus, this study proposes an enhanced RDA that incorporates adaptive memory strategies to strengthen exploration and exploitation. The RDA classifies the population into males (i.e., high-quality solutions) and females or hinds (i.e., lower-quality solutions), with males further divided into commanders and stags. Exploitation occurs through roaring, selecting commanders, and fighting, while exploration involves forming harems and mating commanders with hinds both within and outside their harems. In enhanced RDA, previously mated hinds are adaptively excluded to promote mating with new ones, thereby enhancing exploration and solution quality. Enhanced RDA with an adaptive memory strategy (RDAM) was tested on 87 benchmark instances, outperforming a construction algorithm by 96.5% and the original RDA by 87.3%, and showing competitive results with established methods. The adaptive memory strategy in RDA enhanced the total distance compared with the original RDA, while improving its exploration–exploitation in solving the CVRP, offering theoretical and practical benefits for logistics efficiency.
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