{"title":"A Novel Evolution Optimization Algorithm Using a Multidimensional Geometric Method: Pivot Optimiser","authors":"S. Thongkrairat, V. Chutchavong","doi":"10.5013/IJSSST.A.21.04.13","DOIUrl":null,"url":null,"abstract":"Many optimisation techniques have recently been developed. Several mimic natural activity or another theory, such as the Grey Wolf Optimiser, which emulates wolf hunting mechanisms to find a global minimum, and Particle Swarm Optimisation, which uses birds’ flocking behaviour to avoid each local minimum. Each developed algorithm uses guidelines to improve its mimicry and reach its goal. This work proposes the pivot optimiser, a new evolution optimisation algorithm inspired by the multidimensional geometric method to create a unique evolution in each generation. The goal of this imitation is to make an algorithm suitable for a multi-situation problem with a stable result. The results show that the pivot optimiser outperformed on competitive problems compared with other competitive optimisers. Keywordsoptimisation, algorithm, swarm intelligence, geometric, GWO, PSO","PeriodicalId":14286,"journal":{"name":"International journal of simulation: systems, science & technology","volume":"41 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of simulation: systems, science & technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5013/IJSSST.A.21.04.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Many optimisation techniques have recently been developed. Several mimic natural activity or another theory, such as the Grey Wolf Optimiser, which emulates wolf hunting mechanisms to find a global minimum, and Particle Swarm Optimisation, which uses birds’ flocking behaviour to avoid each local minimum. Each developed algorithm uses guidelines to improve its mimicry and reach its goal. This work proposes the pivot optimiser, a new evolution optimisation algorithm inspired by the multidimensional geometric method to create a unique evolution in each generation. The goal of this imitation is to make an algorithm suitable for a multi-situation problem with a stable result. The results show that the pivot optimiser outperformed on competitive problems compared with other competitive optimisers. Keywordsoptimisation, algorithm, swarm intelligence, geometric, GWO, PSO