{"title":"一种改进的多策略鸽类优化算法","authors":"H. Liao, Huadong Huang","doi":"10.1051/itmconf/20224702002","DOIUrl":null,"url":null,"abstract":"Pigeon-inspired optimization algorithm is easy to fall into local optimization and low convergence accuracy in solving nonlinear optimization problems. In this paper, an improved pigeon-inspired optimization algorithm called Gaussian mixture pigeon-inspired optimization algorithm (GPIO) is proposed. In GPIO, the cubic mapping of chaotic mapping method is used to initialize the pigeon population, which increases the diversity of the population. Gaussian mutation operator is introduced to change the shortage that pigeon swarm algorithm is easy to fall into local optimization, and improve the convergence efficiency of the algorithm. The experimental results of 19 benchmark functions show that the algorithm has better optimization ability than other swarm intelligence algorithms.","PeriodicalId":433898,"journal":{"name":"ITM Web of Conferences","volume":"94 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi strategy improved pigeon-inspired optimization algorithm\",\"authors\":\"H. Liao, Huadong Huang\",\"doi\":\"10.1051/itmconf/20224702002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Pigeon-inspired optimization algorithm is easy to fall into local optimization and low convergence accuracy in solving nonlinear optimization problems. In this paper, an improved pigeon-inspired optimization algorithm called Gaussian mixture pigeon-inspired optimization algorithm (GPIO) is proposed. In GPIO, the cubic mapping of chaotic mapping method is used to initialize the pigeon population, which increases the diversity of the population. Gaussian mutation operator is introduced to change the shortage that pigeon swarm algorithm is easy to fall into local optimization, and improve the convergence efficiency of the algorithm. The experimental results of 19 benchmark functions show that the algorithm has better optimization ability than other swarm intelligence algorithms.\",\"PeriodicalId\":433898,\"journal\":{\"name\":\"ITM Web of Conferences\",\"volume\":\"94 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ITM Web of Conferences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/itmconf/20224702002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ITM Web of Conferences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/itmconf/20224702002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multi strategy improved pigeon-inspired optimization algorithm
Pigeon-inspired optimization algorithm is easy to fall into local optimization and low convergence accuracy in solving nonlinear optimization problems. In this paper, an improved pigeon-inspired optimization algorithm called Gaussian mixture pigeon-inspired optimization algorithm (GPIO) is proposed. In GPIO, the cubic mapping of chaotic mapping method is used to initialize the pigeon population, which increases the diversity of the population. Gaussian mutation operator is introduced to change the shortage that pigeon swarm algorithm is easy to fall into local optimization, and improve the convergence efficiency of the algorithm. The experimental results of 19 benchmark functions show that the algorithm has better optimization ability than other swarm intelligence algorithms.