检索结果-南通市图书馆

Chaotic Social Group Optimization for Structural Engineering Design Problems

Journal of Bionic Engineering 2023年第4期20卷 1852-1877页

作者： anima naik Department of CSE VIITVisakhapatnamAndhra Pradesh530049India

Till now,several novel metaheuristic algorithms are proposed for global *** only specific algorithms have become popular or attracted researchers,who are efficient in solving global optimization problems as well as real-world application *** Social Group Optimization(SGO)algorithm is a new metaheuristic bioinspired algorithm inspired by human social behavior that attracted researchers due to its simplicity and problem-solving *** this study,to deal with the problems of low accuracy and local convergence in SGO,the chaos theory is introduced into the evolutionary process of *** chaotic mapping has certainty,ergodicity,and stochastic property,by replacing the constant value of the self-introspection parameter with chaotic maps,the proposed chaotic social group optimization algorithm increases its convergence rate and resulting *** proposal chaotic SGO is validated through 13 benchmark functions and after that 9 structural engineering design problems have been *** simulated results have been noticed as competent with that of state-of-art algorithms regarding convergence quality and accuracy,which certifies that improved SGO with chaos is valid and feasible.

关键词： Chaos Bionic algorithm Constrained optimization SGO Design problem

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Weighted Teaching-Learning-Based Optimization for Global Function Optimization

引用

Applied Mathematics 2013年第3期4卷 429-439页

作者： Suresh Chandra Satapathy anima naik K. Parvathi Anil Neerukonda Institute of Technology and Sciences Vishakapatnam India Centurion University of Technology and Management Paralakhemundi India Majhighariani Institute of Technology & Science Rayagada India

Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.

关键词： Function Optimization TLBO Evolutionary Computation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Automatic Clustering Using Teaching Learning Based Optimization

引用

Applied Mathematics 2014年第8期5卷 1202-1211页

作者： M. Ramakrishna Murty anima naik J. V. R. Murthy P. V. G. D. Prasad Reddy Suresh C. Satapathy K. Parvathi Andhra University Visakhapatnam India Anil Neerukonda Institute of Technology and Sciences Visakhapatnam India Centurion University of Technology and Management Paralakhemundi India GMR Institute of Technolgy Rajam India Jawaharlal Nehru Technological University Kakinada Kakinada India Majhighariani Institute of Technology & Science Rayaged India

Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization of these approaches also have been tried by researchers. However, the number of optimal clusters and the computational efficiency has still remained open for further research. In this paper, a new optimization technique known as “Teaching-Learning-Based Optimization” (TLBO) is implemented for automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified rather it determines the optimal number of partitions of the data “on the run”. The new AUTO-TLBO algorithms are evaluated on benchmark datasets (collected from UCI machine repository) and performance comparisons are made with some well-known clustering algorithms. Results show that AUTO-TLBO clustering techniques have much potential in terms of comparative results and time of computations.

关键词： Clustering Auto-Clustering TLBO

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：