Modified Reptile Search Algorithm for Resolving Capacitated Vehicle Routing Problem

Abstract

The Capacitated Vehicle Routing Problem (CVRP) is a fundamental optimization difficulty in logistics due to the necessity of efficient route planning alongside vehicle capacity constraints. The research introduces an enhanced Modified Reptile Search Algorithm (MRSA) featuring three significant improvements for better solution quality and faster computational performance. Through a mutual information-based initialization method combined with dynamic control parameter adjustments of α and β to manage exploration and exploitation balance, the study enhances population diversity and employs finite element-based search space partitioning for localized optimization. MRSA utilizes a mutual information-based approach to initialize processes that generate high-quality solutions through initial routes demonstrating significant statistical patterns between customers and nodes. The benchmark datasets composed of 49 instances from Set A and Set B enable the assessment of the proposed MRSA. MRSA demonstrates superior performance to standard RSA by matching or exceeding the best-known solutions in 38 out of 49 cases and reducing the average gap by up to 8.5%. Experimental data show that MRSA exhibits powerful scalability alongside stable efficiency when dealing with intricate CVRP scenarios.