检索结果-内蒙古大学图书馆

IEEE Mediterranean Electrotechnical Conference

作者： Sogol Babaei Nejad Seyyed Hamid Elyas Ali Khamseh Iman Naziri Moghaddam Mehdi Karrari Department of Electrical Engineering Semnan University Department of Electrical Engineering Amir Kabir University of Technology

ISBN: (纸本)9781467307826

Economic load Dispatch (ED) is one of the most important problems in power system operation. It is a nonlinear non-convex problem which stochastic search algorithms seem to be appropriate solutions. This study tries to propose a new method which is derived from the combination of two different algorithms, clonal as the basic algorithm and PSO. The proposed method has been tested on two different systems containing thirteen and forty generators and obtained results have been compared with the results of other stochastic search algorithms. The fascinating results obtained from the comparison ensure the efficiency of the new proposed method.

关键词： Economic Load Dispatch clonal selection algorithm Particle Swarm Optimization

来源：评论

学校读者我要写书评

暂无评论

Function Optimization Based on Improved clonal selection algorithm

Function Optimization Based on Improved Clonal Selection Alg...

引用

第14届中国系统仿真技术及其应用学术年会

作者： Xi CHEN 1,Yuxuan DING 2,Jianjun HE 3 1 School of Information and Communication Engineering,Hunan Institute of Science and Technology,Yueyang,Hunan,China 2 College of Electrical and Electronic Engineering,Huazhong University of Science and Technology,Wuhan,China 3 School of Information Science and Engineering,Central South University,Changsha Hunan,China

An improved clonal selection algorithm is proposed for the two shortcomings of the traditional *** contents are as follows:An adaptive improved mutation operator is *** we put forward an improved selection operator which takes into account two factors of affinity and *** simulation results show the improved algorithm is superior to the traditional clonal selection algorithm in convergence speed and optimizing result.

关键词： clonal selection algorithm affinity weight factor convergence speed function optimization

来源：评论

学校读者我要写书评

暂无评论

A new efficient metaheuristic for solving the Uncapacitated Single Allocation p-Hub Median Problem

A new efficient metaheuristic for solving the Uncapacitated ...

引用

International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA)

作者： Grine, Fatima Zahraa Kamach, Oulaid Sefiani, Naoufal Natl Sch Appl Sci Innovat Technol Lab Tangier Morocco Fac Sci & Technol Engn Innovat & Management Ind Syst Res Team Tangier Morocco

ISBN: (纸本)9781538678374

Hub location problem is an NP-hard problem, it deals with finding the location of hub facilities and allocating the demand nodes to these hub facilities so as to effectively route the demand between any origin-destination pair. In this paper, we study the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) variant of the hub location problems. Thus, we present a new and robust solution based on artificial immune system (AIS) framework for this problem. The outcome of this paper is a new approach in the form of clonal selection algorithm (CSA) to solve this location-allocation problem. The algorithm maintains the feasibility of individuals by using a specific representation. To the best of our knowledge, this methodology of solving the USApHMP is introduced to the literature for the first time.

关键词： Hub location problem USApHMP Logistic Hub Artificial immune system clonal selection algorithm Metaheuristic

来源：评论

学校读者我要写书评

暂无评论

Detecting RTL Trojans using Artificial Immune Systems and High Level Behavior Classification

Detecting RTL Trojans using Artificial Immune Systems and Hi...

引用

3rd Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

作者： Zareen, Farhath Karam, Robert Univ S Florida Dept CSE Tampa FL 33620 USA

ISBN: (纸本)9781538674710

Security assurance in a computer system can be viewed as distinguishing between self and non-self. Artificial Immune Systems (AIS) are a class of machine learning (ML) techniques inspired by the behavior of innate biological immune systems, which have evolved to accurately classify self-behavior from non-self-behavior. This work aims to leverage AIS-based ML techniques for identifying certain behavioral traits in high level hardware descriptions, including unsafe or undesirable behaviors, whether such behavior exists due to human error during development, or due to intentional, malicious circuit modifications, known as hardware Trojans, without the need for a golden reference model. We explore the use of Negative selection and clonal selection, which have historically been applied to malware detection on software binaries, to detect potentially unsafe or malicious behavior in hardware. We present a software tool which analyzes Trojan-inserted benchmarks, extracts their control and data-flow graphs (CDFGs), and uses this to train an AIS behavior model, against which new hardware descriptions may be tested. The proposed model is capable of detecting the specified (Trojan or Trojan-like) behavior with an accuracy of -85% and an average false negative rate of 12.6% for Negative selection and 12.8% for clonal selection.

关键词： Hardware Trojans Hardware Security Artificial Immune Systems CDFG Negative selection algorithm clonal selection algorithm

来源：评论

学校读者我要写书评

暂无评论

Immune Multi-population Firefly algorithm and Its Application in Multimodal Function Optimization 9

Immune Multi-population Firefly Algorithm and Its Applicatio...

引用

9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhou, Jiufang Wu, Jianhui Chen, Hua Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781538665657

In this paper, a novel immune multl-populanon firefly algorithm (IMPFA) is presented to solve multimodal function optlmlzatton problems. The proposed algorithm integrates multi population firefly algorithm (MPFA) with non-uniform mutation clonal selection algorithm (NUMCSA). In each loop iteration, firstly, the MPFA based on multl-populatten learnmg mechanism is used to search globally in the feasible region, and then the NUM CSA is utilized to search locally for improving the accuracy of the sub-optlmal solutions obtained with MPFA. Simulation results show that the IMPFA is extremely effective and increases the precision of solutions.

关键词： immune multi-population fir efl) algorithm multimodal function optimization clonal selection algorithm

来源：评论

学校读者我要写书评

暂无评论

Short-term hydrothermal scheduling using clonal selection algorithm

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第3期33卷 647-656页

作者： Swain, R. K. Barisal, A. K. Hota, P. K. Chakrabarti, R. ITER Dept Elect Engn Bhubaneswar Orissa India VSSUT Dept Elect Engn Burin Orissa India Jadavpur Univ Dept Elect Engn Kolkata India

An efficient optimization procedure based on the clonal selection algorithm (CSA) is proposed for the solution of short-term hydrothermal scheduling problem. CSA, a new algorithm from the family of evolutionary computation, is simple, fast and a robust optimization tool for real complex hydrothermal scheduling problems. Hydrothermal scheduling involves the optimization of non-linear objective function with set of operational and physical constraints. The cascading nature of hydro-plants, water transport delay and scheduling time linkage, power balance constraints, variable hourly water discharge limits, reservoir storage limits, operation limits of thermal and hydro units, hydraulic continuity constraint and initial and final reservoir storage limits are fully taken into account. The results of the proposed approach are compared with those of gradient search (GS), simulated annealing (SA), evolutionary programming (EP), dynamic programming (DP), non-linear programming (NLP), genetic algorithm (GA), improved fast EP (IFEP), differential evolution (DE) and improved particle swarm optimization (IPSO) approaches. From the numerical results, it is found that the CSA-based approach is able to provide better solution at lesser computational effort. (c) 2011 Elsevier Ltd. All rights reserved.

关键词： clonal selection algorithm Differential evolution Hydrothermal scheduling Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Optimization of multiple input-output fuzzy membership functions using clonal selection algorithm

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第3期38卷 1374-1381页

作者： Acilar, A. Merve Arslan, Ahmet Selcuk Univ Eng Arch Fac Dept Comp Engn TR-42003 Konya Turkey

A clonal selection algorithm (clonalG) inspires from clonal selection principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the study is to determine the membership functions. Proposed method has been implemented using a developed clonalG program for a multiple input-output (MI-O) fuzzy system. In this study. GA and binary particle swarm optimization (BPSO) are used for implementing the proposed method as well and they are compared. It has been shown that using clonal selection algorithm is advantageous for finding optimum values of fuzzy membership functions (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Multiple input-output fuzzy membership functions Optimization clonal selection algorithm

来源：评论

学校读者我要写书评

暂无评论

Spectral Element Model Updating for Damage Identification Using clonal selection algorithm

引用

ADVANCES IN STRUCTURAL ENGINEERING 2011年第5期14卷 837-856页

作者： Wang, Ying Zhu, Xinqun Hao, Hong Ou, Jinping Univ Western Australia Sch Civil & Resource Engn Nedlands WA 6009 Australia Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China

A spectral element model updating procedure is presented to identify damage in a structure using Guided wave propagation results. Two damage spectral elements (DSE1 and DSE2) are developed to model the local (cracks in reinforcement bar) and global (debonding between reinforcement bar and concrete) damage in one-dimensional homogeneous and composite waveguide, respectively. Transfer matrix method is adopted to assemble the stiffness matrix of multiple spectral elements. In order to solve the inverse problem, clonal selection algorithm is used for the optimization calculations. Two displacement-based functions and two frequency-based functions are used as objective functions in this study. Numerical simulations of wave propagation in a bare steel bar and in a reinforcement bar without and with various assumed damage scenarios are carried out. Numerically simulated data are then used to identify local and global damage of the steel rebar and the concrete-steel interface using the proposed method. Results show that local damage is easy to be identified by using any considered objective function with the proposed method while only using the wavelet energy-based objective function gives reliable identification of global damage. The method is then extended to identify multiple damages in a structure. To further verify the proposed method, experiments of wave propagation in a rectangular steel bar before and after damage are conducted. The proposed method is used to update the structural model for damage identification. The results demonstrate the capability of the proposed method in identifying cracks in steel bars based on measured wave propagation data.

关键词： damage identification spectral element model updating clonal selection algorithm wave propagation

来源：评论

学校读者我要写书评

暂无评论

Utilizing clonal selection Theory Inspired algorithms and K-Means Clustering for Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption 2nd

引用

2nd International Conference on Soft Computing and Data Mining (SCDM)

作者： Lasisi, Ayodele Ghazali, Rozaida Chiroma, Haruna Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Fed Coll Educ Tech Sch Sci Dept Comp Sci Gombe Gombe State Nigeria

ISBN: (纸本)9783319512815;9783319512792

The prediction of carbon dioxide (CO2) emissions from petroleum consumption inspired and motivated this research. Over the years, the rate of emissions of CO2 continues to multiply, resulting in global warming. This paper thus proposes the use of clonal selection theory inspired algorithms;clonalG and AIRS to forecast global CO2 emissions. The K-means algorithm divides the data into groups of similar and meaningful patterns. Comparative simulations with multi-layer Perceptron, IBk, fuzzy-rough nearest neighbor, and vaguely quantified nearest neighbor reveal that the clonalG and AIRS produced outstanding results, and are able to generate highest detection rates and lowest false alarm rates. As such, gathering useful information with the accurate prediction of CO2 emissions can help to reduce the emission of CO2 contributions to global warming which assist in policies on climate change.

关键词： clonal selection algorithm Artificial immune recognition system Clustering algorithm Carbon dioxide emissions

来源：评论

学校读者我要写书评

暂无评论

Dynamic network structured immune particle swarm optimisation with small-world topology

引用

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2017年第2期9卷 93-105页

作者： Sun, Yifei Jiao, Licheng Deng, Xiaozheng Wang, Rongfang Shanxi Normal Univ Sch Phys & Infomat Technol Xian 710119 Shaanxi Peoples R China Xidian Univ Int Res Ctr Intelligent Percept & Comp Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Particle swarm optimisation (PSO) has attracted much attention and is used to wide applications in different fields in recent years because of its simple concept, easy implementation and quick convergence. However, it suffers from premature convergence since the population's diversity loses quickly. In this paper, a novel and efficient variant of PSO named DNIPSO is proposed which help the diversity of the swarm be preserved via the Newman-Watts small world network topology and the immune learning operator. Initially the topology of the population is the regular network. Then the Newman-Watts small world topology is formed gradually and the swarm evolves simultaneously. The optimisation process contains the population structure dynamics and particle immune learning two parts which mutually promoted effectively in whole population. Furthermore, the immune operator which is based on the clonal selection theory achieves a trade-off between exploration and exploitation abilities. Numerical experiments both on continuous unconstrained and constrained benchmark functions are used to test the performance of DNIPSO. Simulation results show it is effective and robust.

关键词： particle swarm optimisation Newman-Watts small-world model artificial immune system dynamic network structure clonal selection 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：