{"title":"修改了用于全局优化的多宇宙优化器","authors":"D. K. Mishra, V. Shinde","doi":"10.1504/IJSI.2021.10036209","DOIUrl":null,"url":null,"abstract":": In this paper, modified version of multi-verse optimiser (MVO) was suggested and tested on numerical optimisation problems. MVO is an innovative optimisation approach which stimulated from the concepts of cosmology; they are named as white hole, black hole and wormhole. Mathematical modelling of this concept has been carried out to acquire exploitation, exploration and local search. Modification in MVO has been made by introducing concept of dynamic variation in population size (universe). Modified multi-verse optimiser (MMVO) was tested on 16 benchmark functions having different complexity. Statistical comparisons of other algorithms outcomes is depicted that MMVO performs better than other algorithms.","PeriodicalId":44265,"journal":{"name":"International Journal of Swarm Intelligence Research","volume":"13 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Modified multi-verse optimiser used for global optimisation\",\"authors\":\"D. K. Mishra, V. Shinde\",\"doi\":\"10.1504/IJSI.2021.10036209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": In this paper, modified version of multi-verse optimiser (MVO) was suggested and tested on numerical optimisation problems. MVO is an innovative optimisation approach which stimulated from the concepts of cosmology; they are named as white hole, black hole and wormhole. Mathematical modelling of this concept has been carried out to acquire exploitation, exploration and local search. Modification in MVO has been made by introducing concept of dynamic variation in population size (universe). Modified multi-verse optimiser (MMVO) was tested on 16 benchmark functions having different complexity. Statistical comparisons of other algorithms outcomes is depicted that MMVO performs better than other algorithms.\",\"PeriodicalId\":44265,\"journal\":{\"name\":\"International Journal of Swarm Intelligence Research\",\"volume\":\"13 1\",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Swarm Intelligence Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/IJSI.2021.10036209\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Swarm Intelligence Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/IJSI.2021.10036209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Modified multi-verse optimiser used for global optimisation
: In this paper, modified version of multi-verse optimiser (MVO) was suggested and tested on numerical optimisation problems. MVO is an innovative optimisation approach which stimulated from the concepts of cosmology; they are named as white hole, black hole and wormhole. Mathematical modelling of this concept has been carried out to acquire exploitation, exploration and local search. Modification in MVO has been made by introducing concept of dynamic variation in population size (universe). Modified multi-verse optimiser (MMVO) was tested on 16 benchmark functions having different complexity. Statistical comparisons of other algorithms outcomes is depicted that MMVO performs better than other algorithms.
期刊介绍:
The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.