检索结果-内蒙古大学图书馆

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

SWARM INTELLIGENCE 2010年第1期4卷 57-89页

作者： El-Abd, Mohammed Kamel, Mohamed S. Univ Waterloo ECE Dept Waterloo ON N2L 3G1 Canada

Particle Swarm optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking, and that has been successfully used in many applications as an optimization tool. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms which perform a two-step process: building a probabilistic model from which good solutions may be generated and then using this model to generate new individuals. Two distinct research trends that emerged in the past few years are the hybridization of PSO and EDA algorithms and the parallelization of EDAs to exploit the idea of exchanging the probabilistic model information. In this work, we propose the use of a cooperative PSO/EDA algorithm based on the exchange of heterogeneous probabilistic models. The model is heterogeneous because the cooperating PSO/EDA algorithms use different methods to sample the search space. Three different exchange approaches are tested and compared in this work. In all these approaches, the amount of information exchanged is adapted based on the performance of the two cooperating swarms. The performance of the cooperative model is compared to the existing state-of-the-art PSO cooperative approaches using a suite of well-known benchmark optimization functions.

关键词： Particle swarm optimization Estimation of distribution algorithms Cooperative search Hybrid algorithms Probabilistic models migration non-linear function optimization

来源：评论

学校读者我要写书评

暂无评论

Particle Swarm optimization with Adaptive Bounds

Particle Swarm Optimization with Adaptive Bounds

引用

IEEE Congress on Evolutionary Computation (CEC)

作者： El-Abd, Mohammed Kamel, Mohamed S. Amer Univ Kuwait Engn & Sci Div Engn & Sci Div Kuwait Kuwait Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781467315098

Particle Swarm optimization (PSO) is a stochastic optimization approach that originated from early attempts to simulate the behavior of birds looking for food. Estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms that build and maintain a probabilistic model capturing the search space characteristics and continuously use this model to generate new individuals. In this work, we propose a new PSO and EDA hybrid algorithm that uses the particles' distribution in the search space in order to adjust the search space bounds, hence, restricting the particles movement as well as their allowable maximum velocity. The algorithms is augmented with a mechanism to overcome premature convergence and escape local minima. The algorithm is compared to the standard PSO algorithm using a suite of well-known benchmark optimization functions. Experimental results show that the proposed algorithm has a promising performance.

关键词： Particle swarm optimization estimation of distribution algorithms hybrid techniques non-linear function optimization

来源：评论

学校读者我要写书评

暂无评论

Factors governing the behavior of multiple cooperating swarms

Factors governing the behavior of multiple cooperating swarm...

引用

Genetic and Evolutionary Computation Conference

作者： El-Abd, Mohammed Kamel, Mohamed Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)1595930108

This paper investigates the idea of having multiple swarms working separately and cooperating with each other to solve an optimization problem. Many factors that influence the behavior of this approach haven't been properly studied. This paper investigates two factors that affect this approach behavior. These factors are: (i) the communication strategy adopted if the number of swarms is raised above two, and (ii) the number of cooperating swarms. Experiments run on different benchmark optimization functions show that adopting a circular communication strategy gives better results than just sharing the global best of all the swarms. Increasing the number of cooperating swarms provides better results provided that the appropriate synchronization period is selected.

关键词： particle swarm optimization cooperative search non-linear function optimization

来源：评论

学校读者我要写书评

暂无评论

Factors governing the behavior of multiple cooperating swarms 05

Factors governing the behavior of multiple cooperating swarm...

引用

Proceedings of the 7th annual conference on Genetic and evolutionary computation

作者： Mohammed El-Abd Mohamed Kamel University of Waterloo Waterloo ON Canada

ISBN: (纸本)9781595930101

关键词： particle swarm optimization cooperative search non-linear function optimization

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：