EAI Endorsed Transactions on Scalable Information Systems | |
Master-Slave TLBO Algorithm for Constrained Global Optimization Problems | |
Amol Adamuthe1  Sandeep Mane2  Rajshree Omane3  | |
[1] Dept. of CS&Dept. of CSE, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Dist. Sangli, MH, India;IT, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Dist. Sangli, MH, India; | |
关键词: master-slave tlbo algorithm; parallel evolutionary algorithms; gpgpu; constrained benchmark functions; optimization problems; | |
DOI : 10.4108/eai.26-5-2020.166292 | |
来源: DOAJ |
【 摘 要 】
INTRODUCTION: Theteaching-learningbasedoptimization(TLBO)algorithmisarecentlydevelopedalgorithm.Theproposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: Thisresearchaimstodesignamaster-slaveTLBOalgorithmtoimproveitsperformanceandsystemutilization for CEC2006 single-objective benchmark functions. METHODS: TheproposedapproachimplementedusingOpenMPandCUDAC,ahybridprogrammingapproachtoenhancetheutilizationofthesystem’scomputationalresources.Thedeviceutilizationandperformanceoftheproposedapproach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.
【 授权许可】
Unknown