检索结果-内蒙古大学图书馆

2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Hongwei Xu Wulanchabu Vocational College

ISBN: (纸本)9781510823808

This paper proposed an improved stud genetic algorithm using the Opposition-based strategy(SGAO) to improve the performance of the traditional SGA and accelerate its convergence *** SGAO,we use opposition-based approach to initialize the population and to perform mutation with the aim to improve the quality of *** experiments,we use some benchmark functions to the show the performance of the proposed approach and compare it with other algorithms such as genetic algorithm,different evolutionary,particle swarm optimization and stud genetic *** show that SGAO has faster convergence speed and higher solution precision.

关键词： Opposition-based Strategy stud genetic algorithm Optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

stud krill herd algorithm

引用

NEUROCOMPUTING 2014年第0期128卷 363-370页

作者： Wang, Gai-Ge Gandomi, Amir H. Alavi, Amir H. Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Univ Akron Dept Civil Engn Akron OH 44325 USA Michigan State Univ Dept Civil & Environm Engn E Lansing MI 48824 USA

Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH. Krill Herd: A New Bio-Inspired Optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845, 2012.]. This paper represents an optimization method to global optimization using a novel variant of KH. This method is called the stud Krill Herd (SKH). Similar to genetic reproduction mechanisms added to KH method, an updated genetic reproduction schemes, called stud selection and crossover (SSC) operator, is introduced into the KH during the krill updating process dealing with numerical optimization problems. The introduced SSC operator is originated from original stud genetic algorithm. In SSC operator, the best krill, the stud, provides its optimal information for all the other individuals in the population using general genetic operators instead of stochastic selection. This approach appears to be well capable of solving various functions. Several problems are used to test the SKH method. In addition, the influence of the different crossover types on convergence and performance is carefully studied. Experimental results indicate an instructive addition to the portfolio of swarm intelligence techniques. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Global optimization problem Krill herd stud genetic algorithm stud selection and crossover operator Multimodal function

来源：评论

学校读者我要写书评

暂无评论

stud Multi-Verse algorithm 2

Stud Multi-Verse Algorithm

引用

2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Meshkat, Mostafa Parhizgar, Mohsen Islamic Azad Univ Mashhad Branch Dept Artificial Intelligence Mashhad Iran Islamic Azad Univ Dept Artificial Intelligence Ferdows Branch Ferdows Iran

ISBN: (纸本)9781509043309

Recently, a novel bio-inspired optimization algorithm known as Multi-Verse Optimizer (MVO) has been proposed for solving optimization problems based on the fundamental multiverse theory including concepts such as white holes, black holes, and wormholes. The objective of this study was to present an optimization algorithm using MVO as well as the stud selection and crossover (SSe) operator, namely the stud Multi-Verse algorithm (stud MVO), in order to improve the performance of the MVO algorithm. The see operator is originated from the stud genetic algorithm (stud GA), by which the best search agent known as the stud provides optimal information for other search agents in the population using general genetic operators. In order to evaluate the performance of the stud MVO, twenty-three benchmark functions including unimodal, multimodal and fixed-dimension multimodal benchmark functions were used. The comparison of the results indicated that stud MVO outperformed the MVO algorithm in twenty benchmark functions.

关键词： Global optimization problem Metaheuristic Multi-Verse Optimizer stud genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：