Obstacle-Aware Multi-Target Routing for Campus Logistics Using an Improved Mayfly Optimization Algorithm

Ze Yang

doi:10.3390/su18179138

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Obstacle-Aware Multi-Target Routing for Campus Logistics Using an Improved Mayfly Optimization Algorithm

2026-09-07

Ze Yang

Xinyi Cheng

Haomin Wang *

DOI：10.3390/su18179138

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Abstract

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Autonomous mobile robots are increasingly considered for campus delivery and service logistics, where route efficiency can reduce unnecessary travel under spatial constraints. This study develops an obstacle-aware routing framework that combines a 1 m occupancy grid, A* shortest-path computation, and an Improved Mayfly Optimization Algorithm (IMOA). The A* stage constructs a pairwise distance matrix using orthogonal costs of 1, diagonal costs of 2 , an octile heuristic, and a no-corner-cutting rule; IMOA then optimizes the closed visiting order through random-key decoding, elite 2-opt, and stagnation handling. Validation comprises ten independent benchmark instances, the fixed G40 scenario, and a campus-derived G-real application. Under a common budget of 50,000 full-tour evaluations and 30 independent runs, a Friedman test detected overall differences across the ten instances (χ2(7) = 66.2488, p = 8.434 × 10−12). After Holm correction, IMOA significantly outperformed GA, PSO, GWO, ACO, and MOA, showed no significant difference from MS2OPT, and had a worse average rank than the deterministic LKH reference, which achieved the best overall rank. On G-real, IMOA obtained a median distance of 8178.37 m, compared with 8223.99 m for MS2OPT; this difference was not significant, while LKH achieved the lowest deterministic cost of 8076.46 m. A three-instance exploratory ablation ranked IMOA first and consistently identified elite 2-opt as the principal observed improvement source; component-level inference remains exploratory because only three instances were available. These findings establish routing-efficiency gains under the evaluated protocol. Such gains may support more resource-efficient campus logistics, but energy consumption and carbon emissions were not evaluated.

Keywords:

campus logistics

autonomous mobile robots

obstacle-aware routing

traveling salesman problem

swarm intelligence