{"title":"多滴飞伴旅行商问题的两阶段元启发式算法","authors":"Sheng-Zong Chen , Ren-Yong Guo","doi":"10.1016/j.swevo.2025.102001","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, an integer programming model, based on the flight range limitation of the drone, is formulated for the multi-drops flying sidekick traveling salesman problem (mFSTSP). The connection between the mFSTSP and the corresponding traveling salesman problem (TSP) is then explored, providing a basis for solving the problem in two stages. Subsequently, a new two-stage metaheuristic algorithm is proposed. In the first stage, an adaptive large neighborhood search algorithm with the nearest neighbor operator is employed to solve the corresponding TSP. In the second stage, the obtained TSP route is segmented based on the flight range limitation of the drone, and the simulated annealing framework is used to explore the optimal node allocation scheme of each segment in sequence. Numerical experiments are conducted under varying truck-drone speed ratios and diverse drone maximum flight ranges. The experimental results indicate that optimal or near-optimal solutions to the problem can be obtained in a significantly short time. Furthermore, the proposed two-stage metaheuristic algorithm shows remarkable advantages in solving large-scale instances compared with several advanced heuristic algorithms.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102001"},"PeriodicalIF":8.2000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A two-stage metaheuristic algorithm for the multi-drops flying sidekick traveling salesman problem\",\"authors\":\"Sheng-Zong Chen , Ren-Yong Guo\",\"doi\":\"10.1016/j.swevo.2025.102001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this paper, an integer programming model, based on the flight range limitation of the drone, is formulated for the multi-drops flying sidekick traveling salesman problem (mFSTSP). The connection between the mFSTSP and the corresponding traveling salesman problem (TSP) is then explored, providing a basis for solving the problem in two stages. Subsequently, a new two-stage metaheuristic algorithm is proposed. In the first stage, an adaptive large neighborhood search algorithm with the nearest neighbor operator is employed to solve the corresponding TSP. In the second stage, the obtained TSP route is segmented based on the flight range limitation of the drone, and the simulated annealing framework is used to explore the optimal node allocation scheme of each segment in sequence. Numerical experiments are conducted under varying truck-drone speed ratios and diverse drone maximum flight ranges. The experimental results indicate that optimal or near-optimal solutions to the problem can be obtained in a significantly short time. Furthermore, the proposed two-stage metaheuristic algorithm shows remarkable advantages in solving large-scale instances compared with several advanced heuristic algorithms.</div></div>\",\"PeriodicalId\":48682,\"journal\":{\"name\":\"Swarm and Evolutionary Computation\",\"volume\":\"97 \",\"pages\":\"Article 102001\"},\"PeriodicalIF\":8.2000,\"publicationDate\":\"2025-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Swarm and Evolutionary Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210650225001592\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225001592","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A two-stage metaheuristic algorithm for the multi-drops flying sidekick traveling salesman problem
In this paper, an integer programming model, based on the flight range limitation of the drone, is formulated for the multi-drops flying sidekick traveling salesman problem (mFSTSP). The connection between the mFSTSP and the corresponding traveling salesman problem (TSP) is then explored, providing a basis for solving the problem in two stages. Subsequently, a new two-stage metaheuristic algorithm is proposed. In the first stage, an adaptive large neighborhood search algorithm with the nearest neighbor operator is employed to solve the corresponding TSP. In the second stage, the obtained TSP route is segmented based on the flight range limitation of the drone, and the simulated annealing framework is used to explore the optimal node allocation scheme of each segment in sequence. Numerical experiments are conducted under varying truck-drone speed ratios and diverse drone maximum flight ranges. The experimental results indicate that optimal or near-optimal solutions to the problem can be obtained in a significantly short time. Furthermore, the proposed two-stage metaheuristic algorithm shows remarkable advantages in solving large-scale instances compared with several advanced heuristic algorithms.
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.