An improved variable neighborhood search algorithm embedded temporal and spatial synchronization for vehicle and drone cooperative routing problem with pre-reconnaissance

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Swarm and Evolutionary Computation Pub Date : 2024-08-13 DOI:10.1016/j.swevo.2024.101699

Weichang Sun , Zhihao Luo , Xingchen Hu , Witold Pedrycz , Jianmai Shi

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Abstract

Drones are increasingly utilized for transportation reconnaissance due to their expansive field of view, cost-effectiveness, and agility. They can pre-reconnaissance the traveling routes of the ground vehicles carrying valuable goods to ensure the safety of vehicles and their goods. This rises a novel routing problem for the Vehicles and their Pre-Reconnaissance Drones, which is an integrated point and arc routing problem with temporal and spatial synchronization. A mixed integer linear programming model is developed to formulate the problem with complex synchronization constraints. The Variable Neighborhood Search algorithm integrated with the Temporal & Spatial synchronization-based Greedy search and simulated annealing strategy is designed to solve the model. A practical case based on real urban data from Beijing, China, and random instances with different sizes are tested and compared with the proposed algorithm. Computational results indicated that the proposed algorithm can solve the problem efficiently and outperform the simulated annealing algorithm and the greedy algorithm.

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针对具有预侦察功能的车辆和无人机合作路由问题的嵌入时空同步的改进型可变邻域搜索算法

无人机具有视野开阔、成本效益高和机动灵活等优点，越来越多地被用于运输侦察。它们可以预先侦察运载贵重物品的地面车辆的行驶路线，以确保车辆及其货物的安全。这就提出了一个新颖的车辆及其预侦察无人机的路由问题，这是一个时空同步的点弧综合路由问题。我们建立了一个混合整数线性规划模型，以制定具有复杂同步约束条件的问题。为解决该模型，设计了与基于时空同步的贪婪搜索和模拟退火策略相结合的可变邻域搜索算法。基于中国北京的真实城市数据和不同大小的随机实例对所提出的算法进行了实际测试和比较。计算结果表明，所提出的算法可以高效地解决问题，其性能优于模拟退火算法和贪婪算法。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.