MTLBO-MS: Modified teaching learning based optimization on multicore system

2018 4th International Conference on Recent Advances in Information Technology (RAIT) Pub Date : 2018-03-01 DOI:10.1109/RAIT.2018.8389055

U. Balande, D. Shrimankar, Nitesh Funde

{"title":"MTLBO-MS: Modified teaching learning based optimization on multicore system","authors":"U. Balande, D. Shrimankar, Nitesh Funde","doi":"10.1109/RAIT.2018.8389055","DOIUrl":null,"url":null,"abstract":"Teaching-Learning-Based Optimization (TLBO) algorithm is newly developed nature-inspired algorithm for solving large-scale-global optimization problems. The basic TLBO algorithm is modified using the approach of Differential Evolution with Random-Scale Factor (DERSF). This paper presents a Modified Teaching-Learning-Based Optimization algorithm on Multicore System (MTLBO-MS), which is a parallel version of TLBO. Master-worker paradigm is used for MTLBO-MS algorithm in the teacher and learner stage. This proposed algorithm is tested on different unimodal and multimodal unconstrained benchmark functions with diverse characteristics. The proposed algorithm is implemented on multi-core architecture using open multiprocessing (OpenMP). The effectiveness of the MTLBO-MS algorithm is analyzed in terms of statistical value such as best, mean, speedup and efficiency. The experimental outcomes and discussions justify that the proposed MTLBO-MS algorithm has better speedup, efficiency, computational complexity and optimal best or mean value as compared to other evolutionary algorithms.","PeriodicalId":219972,"journal":{"name":"2018 4th International Conference on Recent Advances in Information Technology (RAIT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 4th International Conference on Recent Advances in Information Technology (RAIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAIT.2018.8389055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

Abstract

Teaching-Learning-Based Optimization (TLBO) algorithm is newly developed nature-inspired algorithm for solving large-scale-global optimization problems. The basic TLBO algorithm is modified using the approach of Differential Evolution with Random-Scale Factor (DERSF). This paper presents a Modified Teaching-Learning-Based Optimization algorithm on Multicore System (MTLBO-MS), which is a parallel version of TLBO. Master-worker paradigm is used for MTLBO-MS algorithm in the teacher and learner stage. This proposed algorithm is tested on different unimodal and multimodal unconstrained benchmark functions with diverse characteristics. The proposed algorithm is implemented on multi-core architecture using open multiprocessing (OpenMP). The effectiveness of the MTLBO-MS algorithm is analyzed in terms of statistical value such as best, mean, speedup and efficiency. The experimental outcomes and discussions justify that the proposed MTLBO-MS algorithm has better speedup, efficiency, computational complexity and optimal best or mean value as compared to other evolutionary algorithms.

查看原文本刊更多论文

MTLBO-MS:基于多核系统优化的改进教学

基于教学的优化算法(TLBO)是一种基于自然的求解大规模全局优化问题的新算法。采用随机尺度因子差分进化(DERSF)方法对基本TLBO算法进行了改进。本文提出了一种改进的多核系统基于教学的优化算法(MTLBO-MS)，它是TLBO的并行版本。MTLBO-MS算法在教师和学习者阶段采用主工范式。在具有不同特征的单峰和多峰无约束基准函数上对该算法进行了测试。该算法采用开放多处理(OpenMP)技术在多核架构上实现。从最优、均值、加速和效率等统计值分析了MTLBO-MS算法的有效性。实验结果和讨论表明，与其他进化算法相比，所提出的MTLBO-MS算法具有更好的加速、效率、计算复杂度和最优均值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2018 4th International Conference on Recent Advances in Information Technology (RAIT)

自引率

0.00%

发文量