{"title":"带决策的人工鼠优化:求解旅行商问题的仿生元启发式算法","authors":"Toufik Mzili, I. Mzili, M. E. Riffi","doi":"10.31181/dmame622023644","DOIUrl":null,"url":null,"abstract":"In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.","PeriodicalId":32695,"journal":{"name":"Decision Making Applications in Management and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Artificial rat optimization with decision-making: A bio-inspired metaheuristic algorithm for solving the traveling salesman problem\",\"authors\":\"Toufik Mzili, I. Mzili, M. E. Riffi\",\"doi\":\"10.31181/dmame622023644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.\",\"PeriodicalId\":32695,\"journal\":{\"name\":\"Decision Making Applications in Management and Engineering\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Decision Making Applications in Management and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31181/dmame622023644\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Making Applications in Management and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31181/dmame622023644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Decision Sciences","Score":null,"Total":0}
Artificial rat optimization with decision-making: A bio-inspired metaheuristic algorithm for solving the traveling salesman problem
In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important applications in transportation, logistics, and manufacturing systems. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO through the incorporation of crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.