检索结果-内蒙古大学图书馆

An agent-based parallel approach for the job shop scheduling problem with genetic algorithms

MATHEMATICAL AND COMPUTER MODELLING 2010年第11-12期52卷 1957-1965页

作者： Asadzadeh, Leila Zamanifar, Kamran Univ Isfahan Dept Comp Engn Esfahan Iran

The job shop scheduling problem is one of the most important and complicated problems in machine scheduling. This problem is characterized as NP-hard. The high complexity of the problem makes it hard to find the optimal solution within reasonable time in most cases. Hence searching for approximate solutions in polynomial time instead of exact solutions at high cost is preferred for difficult instances of the problem. Meta-heuristic methods such as genetic algorithms are widely applied to find optimal or near-optimal solutions for the job shop scheduling problem. parallelizing the genetic algorithms is one of the best approaches that can be used to enhance the performance of these algorithms. In this paper, we propose an agent-based parallel approach for the problem in which creating the initial population and parallelizing the genetic algorithm are carried out in an agent-based manner. Benchmark instances are used to investigate the performance of the proposed approach. The results show that this approach improves the efficiency. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Job shop scheduling problem parallel genetic algorithms Agents Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Advanced genetic image encryption algorithms for intelligent transport systems

引用

COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Souici, Ismahane Mahamdioua, Meriama Jacques, Sebastien Ouahabi, Abdeldjalil Univ Jijel Comp Sci Dept LAOTI Lab Jijel 18000 Algeria Univ Tours F-37020 Tours 1 France Univ Tours UMR 1253 iBrain Inserm F-37000 Tours France

Ensuring the security of sensitive or private information is crucial to prevent malicious tampering, especially in multimedia applications like intelligent transport systems (ITS), which are vital components of a smart city. These systems can be vulnerable to traffic management and rerouting techniques that manipulate the images captured by roadside units. To address this challenge, this paper introduces advanced image encryption algorithms designed specifically for securing image manipulation and transmission in roadside ITS units. Initially, a sequential version of the algorithm is proposed, demonstrating a high level of confusion achieved through the chosen coding method (chromosomal representation). This sequential approach results in maximum interference between the original image and its encrypted counterpart, with an entropy level of 7.95, nearing the optimal value of 8. To improve computational efficiency, three additional algorithms are presented, utilizing parallelization based on the islanding model, both with and without migrations. The algorithms are designed to enhance security by increasing confusion and incorporating genetic diffusion. The performance and security of these algorithms are evaluated using established methods such as information entropy, differential attack analysis, and key space analysis. Our algorithms have also shown a strong ability to maintain performance and robustness even in the presence of noise. Furthermore, they exhibit superior resistance to attacks compared to recent competitive approaches. In summary, the proposed algorithms offer robust protection against image manipulation and unauthorized access in roadside ITS units, thereby contributing to the overall security and reliability of smart city infrastructure.

关键词： Cryptography genetic algorithms parallel genetic algorithms Island model Intelligent transportation systems

来源：评论

学校读者我要写书评

暂无评论

MANUFACTURING CELL-FORMATION USING SIMILARITY COEFFICIENTS AND A parallel genetic TSP ALGORITHM - FORMULATION AND COMPARISON

引用

MATHEMATICAL AND COMPUTER MODELLING 1995年第12期21卷 61-73页

作者： BALAKRISHNAN, J JOG, PD DEPAUL UNIV DEPT COMP SCICHICAGOIL 60604

Many algorithms have been proposed to form manufacturing cells from component routings. However, many of these do not have the capability of solving large problems. We propose a procedure using similarity coefficients and a parallel genetic implementation of a TSP algorithm that is capable of solving large problems of up to 1000 parts and 1000 machines. In addition, we also compare our procedure with many existing procedures using nine well-known problems from the literature. The results show that the proposed procedure compares well with the existing procedures and should be useful to practitioners and researchers.

关键词： CELLULAR MANUFACTURING TRAVELING SALESMAN PROBLEM parallel genetic algorithms GROUP TECHNOLOGY SIMILARITY COEFFICIENTS

来源：评论

学校读者我要写书评

暂无评论

Accelerating genetic algorithm computation in tree shaped parallel computer

引用

JOURNAL OF SYSTEMS ARCHITECTURE 1996年第1期42卷 19-36页

作者： Hamalainen, T Klapuri, H Saarinen, J Ojala, P Kaski, K TAMPERE UNIV TECHNOL SIGNAL PROC LABFIN-33101 TAMPEREFINLAND SIERRA SEMICOND SAN JOSECA 95132

Realizations of genetic algorithms (GAs) in a tree shape parallel computer architecture are presented using different levels of parallelism. In addition, basic models for parallel GAs are considered, The tree shape parallel computer system, GAPA (genetic Algorithm parallel Accelerator) with special hardware for GA computation, is described in detail. Also mappings for centralized and distributed GA models are given and their performance has been measured for different population sizes.

关键词： parallel genetic algorithms parallel implementation parallel computing tree shape architecture

来源：评论

学校读者我要写书评

暂无评论

Multiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computers

引用

HYDROLOGICAL SCIENCES JOURNAL 2005年第6期50卷 1069-1087页

作者： Cheng, CT Wu, XY Chau, KW Dalian Univ Technol Dept Civil Engn Dalian 116024 Peoples R China Hong Kong Polytech Univ Dept Civil & Struct Engn Kowloon Hong Kong Peoples R China

genetic algorithms are among of the global optimization schemes that have gained popularity as a means to calibrate rainfall-runoff models. However, a conceptual rainfall-runoff model usually includes 10 or more parameters and these are interdependent, which makes the optimization procedure very time-consuming. This may result in the premature termination of the optimization process which will prejudice the quality of the results. Therefore, the speed of optimization procedure is crucial in order to improve the calibration quality and efficiency. A hybrid method that combines a parallel genetic algorithm with a fuzzy optimal model in a cluster of computers is proposed. The method uses the fuzzy optimal model to evaluate multiple alternatives with multiple criteria where chromosomes are the alternatives, whilst the criteria are flood performance measures. In order to easily distinguish the performance of different alternatives and to address the problem of non-uniqueness of optimum, two fuzzy ratios are defined. The new approach has been tested and compared with results obtained by using a two-stage calibration procedure. The current single procedure produces similar results, but is simpler and automatic. Comparison of results between the serial and parallel genetic algorithms showed that the current methodology can significantly reduce the overall optimization time and simultaneously improve the solution quality.

关键词： calibration cluster fuzzy evaluation multiple criteria parallel genetic algorithms rainfall-runoff model

来源：评论

学校读者我要写书评

暂无评论

Towards scalable rough set based attribute subset selection for intrusion detection using parallel genetic algorithm in MapReduce

引用

SIMULATION MODELLING PRACTICE AND THEORY 2016年 64卷 18-29页

作者： El-Alfy, El-Sayed M. Alshammari, Mashaan A. King Fahd Univ Petr & Minerals Coll Comp Sci & Engn Dhahran 31261 Saudi Arabia Univ Hail Informat & Comp Sci Dept Hail Saudi Arabia

Attribute subset selection based on rough sets is a crucial preprocessing step in data mining and pattern recognition to reduce the modeling complexity. To cope with the new era of big data, new approaches need to be explored to address this problem effectively. In this paper, we review recent work related to attribute subset selection in decision-theoretic rough set models. We also introduce a scalable implementation of a parallel genetic algorithm in Hadoop MapReduce to approximate the minimum reduct which has the same discernibility power as the original attribute set in the decision table. Then, we focus on intrusion detection in computer networks and apply the proposed approach on four datasets with varying characteristics. The results show that the proposed model can be a powerful tool to boost the performance of identifying attributes in the minimum reduct in large-scale decision systems. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Attribute subset selection Rough sets Hybrid methods Minimum reduct Big data MapReduce parallel genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

A general framework to understand parallel performance in heterogeneous clusters: analysis of a new adaptive parallel genetic algorithm

引用

JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2005年第1期65卷 48-57页

作者： Bazterra, VE Cuma, M Ferraro, MB Facelli, JC Univ Utah Ctr High Performance Comp Salt Lake City UT 84112 USA Univ Buenos Aires Fac Ciencias Exactas & Nat Dept Fis RA-1428 Buenos Aires DF Argentina

This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. (C) 2004 Elsevier Inc. All rights reserved.

关键词： heterogeneous parallel environment parallel genetic algorithms performance analysis

来源：评论

学校读者我要写书评

暂无评论

A hierarchical parallel genetic approach for the graph coloring problem

引用

APPLIED INTELLIGENCE 2013年第3期39卷 510-528页

作者： Abbasian, Reza Mouhoub, Malek Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada

Graph Coloring Problems (GCPs) are constraint optimization problems with various applications including time tabling and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. We propose a hierarchical approach based on parallel genetic algorithms (PGAs) to solve the GCP. We call this new approach Hierarchical PGAs (HPGAs). In addition, we have developed a new operator designed to improve PGAs when solving constraint optimization problems in general and GCPs in particular. We call this new operator genetic Modification (GM). Using the properties of variables and their relations, GM generates good individuals at each iteration and inserts them into the PGA population in the hope of reaching the optimal solution sooner. In the case of the GCP, the GM operator is based on a novel Variable Ordering Algorithm (VOA) that we propose. Together with the new crossover and the estimator of the initial solution we have developed, GM allows our solving approach to converge towards the optimal solution sooner than the well known methods for solving the GCP, even for hard instances. This was indeed clearly demonstrated by the experiments we conducted on the GCP instances taken from the well known DIMACS website.

关键词： parallel genetic algorithms Graph coloring problem

来源：评论

学校读者我要写书评

暂无评论

parallel genetic algorithm based adaptive line enhancement schemes for extraction of power frequency signals

引用

IETE JOURNAL OF RESEARCH 2004年第6期50卷 381-388页

作者： Nanda, PK Ghose, B Sahu, PK Natl Inst Technol Dept Elect Engn Rourkela 769008 Orissa India Rourkela Steel Plant Steel Melting Shop Rourkela 769008 India

In this article, we propose two new adaptive line enhancement schemes for tracking the power frequency signal corrupted with broad-band noise. The first scheme named as Hybrid parallel genetic Algorithm Based Line Enhancer (HPGABLE) employs parallel genetic Algorithm (PGA) and Least Mean Square (LMS) Algorithm to obtain the optimal set of filter coefficients while the delay is selected on an ad hoc basis. In the second proposed Recursive Hybrid parallel genetic Algorithm Based Line Enhancer (RHPGABLE) scheme the delay as well as the filter coefficients are estimated recursively. In the recursion of the proposed RHPGABLE algorithm, parallel genetic Algorithm (PGA) based on coarse grained approach is employed for estimating the delay while the filter coefficients are estimated by PGA and LMS Algorithm. RHPGABLE is an unsupervised scheme in the sense that no a priori knowledge of delay, filter coefficients and the associated training signal component is assumed to be available. The proposed schemes could be successfully tested on both synthetic data as well as the data obtained from the Steel Melting Shop of Rourkela Steel Plant.

关键词： parallel genetic algorithms adaptive filters

来源：评论

学校读者我要写书评

暂无评论

An inverse parallel genetic algorithm for the identification of skin/core debonding in honeycomb aluminium panels

引用

STRUCTURAL CONTROL & HEALTH MONITORING 2015年第12期22卷 1426-1439页

作者： Meruane, V. delFierro, V. Univ Chile Dept Mech Engn Santiago Chile

Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, because of manufacturing defects or impact loads, these structures can experience imperfect bonding or debonding between the skin and the honeycomb core. Instances of debonding reduce the bending stiffness of the composite panel, which causes detectable changes in its vibration characteristics. This article presents a new methodology to identify debonded regions in aluminium honeycomb panels that uses an inverse algorithm based on parallel genetic algorithms. The honeycomb panels are modelled with finite elements using a simplified three-layer shell model. The adhesive layer between the skin and core is modelled using linear springs, with reduced rigidity for the debonded sectors. The algorithm is validated using experimental data from an aluminium honeycomb panel containing different damage scenarios. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： sandwich structures debonding honeycomb parallel genetic algorithms damage assessment

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：