检索结果-内蒙古大学图书馆

2017 3rd IEEE International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Yong Peng Xiaoli Zhang School of Hydraulic Engineering Dalian University of Technology School of Water Conservancy North China University of Water Resources and Electric

ISBN: (纸本)9781538604854

Based on the biological mechanism of immune algorithm, an improved immune genetic algorithm is proposed, in which particle swarm optimization is taken as global searching strategy to improve the global search ability of the immune genetic algorithm, and progressive optimization algorithm is used for evolving operation of control strategy to improve its local search ability. At the same time, because of self-regulatory mechanism of immune algorithm based on concentration, group diversity can be improved and algorithm can be avoided to fall into local optimal solution too early.

关键词： hydropower optimal operation immune algorithm particle swarm optimization the Ertan hydropower station

来源：评论

学校读者我要写书评

暂无评论

The Application Of immune Clone algorithm In Network Intrusion Detection 16

The Application Of Immune Clone Algorithm In Network Intrusi...

引用

16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)

作者： Zhang, Xuekun An, Jing Wang, Yongbin Liu, Wen Commun Univ China Informat Engn Sch Beijing 100024 Peoples R China Minist Culture Key Lab Acoust Visual Technol & Intelligent Contr Beijing 100024 Peoples R China Beijing Key Lab Modern Entertainment Technol Beijing 100024 Peoples R China

ISBN: (纸本)9781509055074

At present, most of the key algorithms of the existing intrusion detection system adopt simple matching technology, only detect the known attack and the false positive and false negative rate is high. The immune cloning algorithm is a search algorithm for iterative process with generating and detecting. In the iterative process, immune cloning algorithm, under the premise of reserving the best individuals of the previous generation, is a global convergence[1]. After further research and improvement based on the operation mechanism and principle of the immune-based intrusion detection system, immune cloning algorithm is applied to the intrusion detection system, which has a very important significance in improving the emergency response capability of the network security system, mitigating the harm of the network attack and enhancing the system's capacity to fight back and so on. This paper, the immune cloning algorithm is used to optimize the detection of the intrusion detection system, use MATLAB software to carry out simulation and obtain the result analysis, and compare with the performance difference of the detector before and after the improvement according to the simulation results.

关键词： intelligent detector immune algorithm network intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Resource-constrained multi-project scheduling problems considering time-dependent reliability of resources: a new immune genetic local search algorithm

引用

KYBERNETES 2025年

作者： Farahmand-Mehr, Mohammad Mousavi, Seyed Meysam Shahed Univ Dept Ind Engn Tehran Iran

PurposeThe purpose of this study is to investigate resource-constrained multi-project scheduling problems (RCMPSP) involving uncertainty in the form of time-dependent renewable resource reliability. A key focus is to minimize the makespan (completion time) of projects when resources can become unavailable or fail over time at non-constant rates. Accounting for realistic resource reliability seeks to provide scheduling solutions that better reflect potential delays in practical multi-project ***/methodology/approachA new discrete-time binary integer programming formulation of RCMPSP is expanded to include time-dependent resource reliability and simultaneously evaluate the time-dependent failure rate and constant repair rate of a resource. A new hybrid immune genetic algorithm with local search (HIGALS) is developed to solve this NP-hard problem. HIGALS incorporates a new coding mechanism, initialization method and local search *** case study tests the proposed HIGALS approach. The validity of the mathematical model is confirmed by solving small-sized problems with GAMS software. The proposed HIGALS algorithm is validated by solving small-sized problems and comparing its solutions with GAMS. The superiority of HIGALS is demonstrated by comparing its solutions with six basic algorithms on medium- and large-sized problems. Results show that HIGALS outperforms existing algorithms, achieving an average reduction in makespan of over 11.79%, while maintaining the advantages of genetic, immune and local search algorithms and avoiding their *** implicationsConsidering time-dependent resource reliability can help project managers plan for disruptions and delays in resource-critical projects. HIGALS provides decision support for robust multi-project ***/valueThis study contributes to the field by investigating RCMPSP with time-dependent renewable resource reliability, which reflects real-world uncertainty mor

关键词： Multi-project scheduling problems Resources reliability Genetic algorithm Local search immune algorithm

来源：评论

学校读者我要写书评

暂无评论

Learning Bayesian network structure with immune algorithm

引用

Journal of Systems Engineering and Electronics 2015年第2期26卷 282-291页

作者： Zhiqiang Cai Shubin Si Shudong Sun Hongyan Dui Ministry of Education Key Laboratory of Contemporary Design and Integrated Manufacturing Technology School of Mechatronics Northwestern Polytechnical University

Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.

关键词： structure learning Bayesian network immune algorithm local optimal structure vaccination

来源：评论

学校读者我要写书评

暂无评论

An improved hybrid immune algorithm for mechanism kinematic chain isomorphism identification in intelligent design

引用

SOFT COMPUTING 2015年第1期19卷 217-223页

作者： Yang, Ping Zeng, Kehan Li, Chunquan Yang, Jianming Wang, Shuting Jiangsu Univ Sch Mech Engn Zhenjiang 212013 Peoples R China Huizhou Univ Dept Comp Sci Huizhou 516007 Peoples R China Mem Univ Newfoundland Fac Engn & Appl Sci St John NF A1B 3X5 Canada Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China

In intelligent mechanism design, isomorphism identification of mechanism kinematic chains (IIMKC) is aimed at avoiding repeated mechanism design and is proved to be an NP-complete problem. In this paper, kinematic chains are represented by graphs. An improved hybrid immune algorithm, which integrates the clonal selection immune algorithm with genetic algorithm and the local search algorithm, is proposed to solve IIMKC problem. Moreover, the novel saving and updating operator is proposed to save the best antibodies and maintain a diverse repertoire of antibodies for improving performance of clonal selection. In addition, the pseudo-crossover operator is introduced to enhance the efficiency of genetic algorithm. Simulation results validate the high efficiency and robustness of the hybrid immune algorithm.

关键词： Mechanism kinematic chain isomorphism immune algorithm Clonal selection Genetic algorithm Local search

来源：评论

学校读者我要写书评

暂无评论

A double-module immune algorithm for multi-objective optimization problems

引用

APPLIED SOFT COMPUTING 2015年 35卷 161-174页

作者： Liang, Zhengping Song, Ruizhen Lin, Qiuzhen Du, Zhihua Chen, Jianyong Ming, Zhong Yu, Jianping Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

Multi-objective optimization problems (MOPs) have become a research hotspot, as they are commonly encountered in scientific and engineering applications. When solving some complex MOPs, it is quite difficult to locate the entire Pareto-optimal front. To better settle this problem, a novel double-module immune algorithm named DMMO is presented, where two evolutionary modules are embedded to simultaneously improve the convergence speed and population diversity. The first module is designed to optimize each objective independently by using a sub-population composed with the competitive individuals in this objective. Differential evolution crossover is performed here to enhance the corresponding objective. The second one follows the traditional procedures of immune algorithm, where proportional cloning, recombination and hyper-mutation operators are operated to concurrently strengthen the multiple objectives. The performance of DMMO is validated by 16 benchmark problems, and further compared with several multi-objective algorithms, such as NSGA-II, SPEA2, SMSEMOA, MOEA/D, SMPSO, NNIA and MIMO. Experimental studies indicate that DMMO performs better than the compared targets on most of test problems and the advantages of double modules in DMMO are also analyzed. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Double-module framework immune algorithm Differential evolution

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid multi-objective immune algorithm with adaptive differential evolution

引用

COMPUTERS & OPERATIONS RESEARCH 2015年第Oct.期62卷 95-111页

作者： Lin, Qiuzhen Zhu, Qingling Huang, Peizhi Chen, Jianyong Ming, Zhong Yu, Jianping Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive differential evolution, named ADE-MOIA, in which the introduction of differential evolution (DE) into multi-objective immune algorithm (MOIA) combines their respective advantages and thus enhances the robustness to solve various kinds of MOPs. In ADE-MOIA, in order to effectively cooperate DE with MOIA, we present a novel adaptive DE operator, which includes a suitable parent selection strategy and a novel adaptive parameter control approach. When performing DE operation, two parents are respectively picked from the current evolved and dominated population in order to provide a correct evolutionary direction. Moreover, based on the evolutionary progress and the success rate of offspring, the crossover rate and scaling factor in DE operator are adaptively varied for each individual. The proposed adaptive DE operator is able to improve both of the convergence speed and population diversity, which are validated by the experimental studies. When comparing ADE-MOIA with several nature-inspired heuristic algorithms, such as NSGA-II, SPEA2, AbYSS, MOEA/D-DE, MIMO and (DMOPSO)-M-2, simulations show that ADE-MOIA performs better on most of 21 well-known benchmark problems. (C) 2015 Published by Elsevier Ltd.

关键词： Multi-objective optimization immune algorithm Differential evolution Adaptive parameter control

来源：评论

学校读者我要写书评

暂无评论

A Multi-Learning immune algorithm for Numerical Optimization

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2015年第1期E98A卷 362-377页

作者： Wang, Shuaiqun Gao, Shangce Aorigele Todo, Yuki Tang, Zheng Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Toyama Univ Fac Engn Toyama 9308555 Japan Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

The emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

关键词： artificial immune algorithm mutation operators immune algorithm hybridization multi-learning

来源：评论

学校读者我要写书评

暂无评论

Emergency Avoidance Control System for an Automatic Vehicle - Slip Ratio Control Using Sliding Mode Control and Real-Number-Coded immune algorithm -

引用

JOURNAL OF ROBOTICS AND MECHATRONICS 2015年第6期27卷 645-652页

作者： Hamaguchi, Masafumi Taniguchi, Takao Shimane Univ Interdisciplinary Grad Sch Sci & Engn 1060 Nishikawatsu Cho Matsue Shimane 6908504 Japan

The automotive industry facilitates research and development on intelligent transport systems. One area researched intensively to enhance passenger safety is the prevention of collisions by controlling steering and braking precisely. In this study, we assume that an automatic vehicle travelling on a highway is on a collision course with an obstacle. The purpose of this research is combining steering and braking to find a set of operations the vehicle can follow to avoid the projected collision. To do this, we propose slip ratio control using sliding mode control using a real-number-coded immune algorithm (IA). CarSim (produced by Mechanical Simulation Company) provides full vehicle dynamics with 27 degrees of freedom adopted as a vehicle model. Operation waveforms are generated by linear interpolation through designated data points. The IA, which is a coded real-number expression, is used to determine data points. Our proposal's efficiency is verified through emergency avoidance simulation using CarSim. Simulation results demonstrate operation that keeps tires from skidding using slip ratio control and halting the vehicle in the shortest braking distance possible.

关键词： immune algorithm emergency avoidance sliding mode control slip ratio control

来源：评论

学校读者我要写书评

暂无评论

ICCO: immune clustering-based coverage optimisation algorithm in wireless sensor networks

引用

INTERNATIONAL JOURNAL OF INTERNET PROTOCOL TECHNOLOGY 2022年第2期15卷 76-93页

作者： Li, Hongbing Liu, Xiaolong Huang, Meng Chen, Qiang Chen, Liwan Chongqing Three Gorges Univ Key Lab Intelligent Informat Proc & Control Chongqing Municipal Inst Higher Educ Chongqing 404120 Peoples R China Chongqing Three Gorges Univ Chongqing Engn Res Ctr Internet Things & Intellig Chongqing 404120 Peoples R China Chongqing Three Gorges Univ Chongqing Collaborat Innovat Subctr Geol Environm Chongqing 404120 Peoples R China Chongqing Three Gorges Univ Chongqing Key Lab Geol Environm Monitoring & Disa Chongqing 404120 Peoples R China Chongqing Three Gorges Univ Key Lab Intelligent Informat Proc & Control Chong Chongqing 404120 Peoples R China

Characteristics of network itself and unpredictability of its working scenario may greatly affect the clustering topology and coverage performance, and further affect transmission reliability and robustness of the network. Topology control and coverage optimisation are the prior issues needed to be solved in wireless sensor network which can affect key performance of whole network. Biological immune algorithm is adopted as a novel effective method to the issues of clustering topology control and coverage optimisation in networks deployed by the static and movable nodes. immune Clustering and Coverage Optimisation algorithm (ICCO) is proposed which includes immune Clustering Optimisation algorithm (IClO) and immune Coverage Optimisation algorithm (ICoO). First, immune efficient clustering is completed in the scenario of static senor nodes and then hierarchical clustering topology control is established. Then, network coverage is optimised through dynamically and iteratively adjusting the positions of mobile sensor nodes by ICoO which is a multi-objective optimisation issue. Network coverage model and energy consumption model is established to analyse the network performance including algorithms' convergence, energy consumption and network area coverage. Simulations show that ICCO has realised the maximum coverage based on the robust immune topology with improved performance of fault tolerance by adjustment of movable nodes.

关键词： wireless sensor networks immune clustering coverage optimisation immune 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：