Xinyang Du, Ruibin Bai, Tianxiang Cui, R. Qu, Jiawei Li
{"title":"竞争旅行商问题的改进蚁群算法","authors":"Xinyang Du, Ruibin Bai, Tianxiang Cui, R. Qu, Jiawei Li","doi":"10.1109/CEC55065.2022.9870414","DOIUrl":null,"url":null,"abstract":"A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims to collect as more rewards as possible with the minimum traveling distance. There is still not effective algorithms for this complicated decision making problem. We investigate an improved ant colony approach for the competitive traveling sales-men problem which adopts a time dominance mechanism and a revised pheromone depositing method to improve the quality of solutions with less computational complexity. Simulation results show that the proposed algorithm outperforms the state of art algorithms.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem\",\"authors\":\"Xinyang Du, Ruibin Bai, Tianxiang Cui, R. Qu, Jiawei Li\",\"doi\":\"10.1109/CEC55065.2022.9870414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims to collect as more rewards as possible with the minimum traveling distance. There is still not effective algorithms for this complicated decision making problem. We investigate an improved ant colony approach for the competitive traveling sales-men problem which adopts a time dominance mechanism and a revised pheromone depositing method to improve the quality of solutions with less computational complexity. Simulation results show that the proposed algorithm outperforms the state of art algorithms.\",\"PeriodicalId\":153241,\"journal\":{\"name\":\"2022 IEEE Congress on Evolutionary Computation (CEC)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Congress on Evolutionary Computation (CEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC55065.2022.9870414\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Congress on Evolutionary Computation (CEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC55065.2022.9870414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem
A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims to collect as more rewards as possible with the minimum traveling distance. There is still not effective algorithms for this complicated decision making problem. We investigate an improved ant colony approach for the competitive traveling sales-men problem which adopts a time dominance mechanism and a revised pheromone depositing method to improve the quality of solutions with less computational complexity. Simulation results show that the proposed algorithm outperforms the state of art algorithms.