Search | Engineering

Subscribe Submit

Home Journals Focus Achievement Fronts About Us 中文版

Resource Type

Journal Article 291

Year

2023 19

2022 15

2021 24

2020 16

2019 17

2018 20

2017 35

2016 22

2015 4

2014 7

2013 6

2012 4

2011 3

2010 4

2009 6

2008 10

2007 16

2006 16

2005 11

2004 10

open ︾

Keywords

genetic algorithm 12

Genetic algorithm 4

Artificial intelligence 3

Deep learning 3

ANN 2

BP algorithm 2

Convolutional neural network 2

Optimization 2

Reinforcement learning 2

genetic algorithms 2

learning algorithm 2

motion compensation 2

neural network 2

optimization 2

scanning microwave radiometer 2

1) model 1

3D path planning 1

3D printing 1

5G 1

open ︾

Search scope:

排序： Display mode:

Firefly algorithm with division of roles for complex optimal scheduling Research Articles

Jia Zhao, Wenping Chen, Renbin Xiao, Jun Ye,zhaojia925@163.com,chen_9731@163.com,rbxiao@hust.edu.cn,yejun68@sina.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 10, Pages 1311-1333 doi: 10.1631/FITEE.2000691

Abstract: A single strategy used in the cannot effectively solve the complex problem. Thus, we propose the FA with (DRFA). Herein, fireflies are divided into leaders, developers, and followers, while a learning strategy is assigned to each role: the leader chooses the greedy ; the developer chooses two leaders randomly and uses the strategy for local development; the follower randomly selects two excellent particles for global exploration. To improve the efficiency of the fixed step size used in FA, a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages. Role division can balance the development and exploration ability of the algorithm. The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems. The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of of cascade reservoirs.

Keywords：萤火虫算法；角色分工；柯西突变；精英邻域搜索；优化调度

HTML PDF Collect

Optimized deployment of a radar network based on an improved firefly algorithm Regular Papers

Xue-jun ZHANG, Wei JIA, Xiang-min GUAN, Guo-qiang XU, Jun CHEN, Yan-bo ZHU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 3, Pages 425-437 doi: 10.1631/FITEE.1800749

Abstract:

The threats and challenges of unmanned aerial vehicle (UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm (FA) and four recently proposed FA variants.

Keywords： Improved firefly algorithm Radar surveillance network Deployment optimization Unmanned aerial vehicle (UAV) invasion defense

HTML PDF Collect

Neosporosis: An Overview of Its Molecular Epidemiology and Pathogenesis Review

Asis Khan, Jahangheer S. Shaik, Patricia Sikorski, Jitender P. Dubey, Michael E. Grigg

Engineering 2020, Volume 6, Issue 1, Pages 10-19 doi: 10.1016/j.eng.2019.02.010

Abstract:

Neospora caninum (N. caninum), a cyst-forming protozoan parasite, is a major cause of bovine abortions and neonatal mortality worldwide. N. caninum has a broad intermediate host range, and its sexual cycle occurs exclusively in canids. Another species of Neospora, N. hughesi, has been identified and causes myeloencephalitis in horses. Although molecular epidemiology studies are in their infancy, the 18S rRNA and ITS1 regions within the ssuRNA and an N. caninum species-specific DNA probe (pNc5) have been used extensively to differentiate Neospora from other closely related apicomplexan parasites. While these repetitive regions have higher sensitivity and specificity than housekeeping or antigen genes, they suffer from low discriminatory power and fail to capture intra-species diversity. Similarly, although multiple minisatellite or microsatellite marker studies have shown clear geographic substructures within Neospora, strains are often misclassified due to a convergence in the size of different alleles at microsatellite loci, known as homoplasy. Only one strain, N. caninum Liverpool (Nc-Liv), has been genome sequenced and compared with its closest relative, Toxoplasma gondii (T. gondii). Hence, detailed population genomics studies based on wholegenome sequences from multiple strains worldwide are needed in order to better understand the current population genetic structure of Neospora, and ultimately to determine more effective vaccine candidates against bovine neosporosis. The aim of this review is to outline our current understanding of the molecular epidemiology and genomics of Neospora in juxtaposition with the closely related apicomplexan parasites Hammondia hammondi and T. gondii.

Keywords： Neosporosis Molecular epidemiology Population genetics Genomics Host response Vaccine

HTML PDF Collect

Optimization and its realization of anneal-genetic algorithm

Wang Ying

Strategic Study of CAE 2008, Volume 10, Issue 7, Pages 57-59

Abstract:

A method that uses annealing algorithm to improve the inefficient local search of genetic algorithm is proposed. That method bases on analysis of the advantages and disadvantages of the annealing and the genetic algorithm. The algorithm optimization is more rapidly in precision after annealing algorithm integration with the genetic algorithm. By examples of cement ratio works, compared with results of the simple algorithm, it is effectively.

Keywords： genetic algorithm simulated annealing algorithm genetic algorithm improvement

HTML PDF Collect

RNA-Based Biocontrols—A New Paradigm in Crop Protection Review

Matthew Bramlett, Geert Plaetinck, Peter Maienfisch

Engineering 2020, Volume 6, Issue 5, Pages 522-527 doi: 10.1016/j.eng.2019.09.008

Abstract:

Modern agribusiness plays a vital role in safeguarding and improving the production, quality, and quantity of food, feed, fiber, and fuel. Growing concerns over the impact of chemical pesticides on health and the environment have stimulated the industry to search for alternative and greener solutions. Over the last years, the RNA interference (RNAi) process has been identified as a very promising new approach to complement the arsenal of foliar spray, soil, or seed treatments applied as chemical and biological pest control agents, and of plant-incorporated protectants (PIPs). RNA-based active ingredients (AIs) possess a unique mode of action and can be implemented via both genetic modification (GM) and biocontrol approaches. RNA-based AIs promise to deliver the selectivity and sustainability desired in future crop protection agents. This is due to their utilization of a natural process to exert control and their high level of selectivity, which leads to reduced risk for non-target organisms (NTOs). This review discusses the advantages and limitations of RNA-based solutions in crop protection and recent research progress toward RNA-based biocontrols against the Colorado potato beetle (CPB), corn rootworm (CRW), and soy stink bug (SSB). Many challenges still exist on the road to the implementation of a broad range of RNA-based products and their widespread use and application. Despite these challenges, it can be expected that RNA-based AIs will become valuable new tools complementing the current arsenal of crop-protection solutions.

Keywords： RNA-based biocontrols RNA interference (RNAi) Colorado potato beetle (CPB) Corn rootworm (CRW) Soy stink bug (SSB)

HTML PDF Collect

Solving Knapsack Problem by Hybrid Particle Swarm Optimization Algorithm

Gao Shang,Yang Jingyu

Strategic Study of CAE 2006, Volume 8, Issue 11, Pages 94-98

Abstract:

The classical particle swarm optimization is a powerful method to find the minimum of a numerical function, on a continuous definition domain. The particle swarm optimization algorithm combining with the idea of the genetic algorithm is recommended to solve knapsack problem. All the 6 hybrid particle swarm optimization algorithms are proved effective. Especially the hybrid particle swarm optimization algorithm derived from across strategy A and mutation strategy C is a simple yet effective algorithm and it has been applied successfully to investment problem. It can easily be modified for any combinatorial problem for which there has been no good specialized algorithm.

Keywords： particle swarm algorithm knapsack problem genetic algorithm mutation

HTML PDF Collect

Quantum coding genetic algorithm based on frog leaping

Xu Bo,Peng Zhiping,Yu Jianping and Ke Wende

Strategic Study of CAE 2014, Volume 16, Issue 3, Pages 108-112

Abstract:

The determinations of the rotation phase of quantum gates and mutation probability are the two main issues that restrict the efficiency of quantum genetic algorithm. This paper presents a quantum real coding genetic algorithm（QRGA）. QRGA used an adaptive means to adjust the direction and the size of the rotation angle of quantum rotation gate. In order to ensure the direction of evolution and population diversity，the mutation probability is guided based on the step of frog leaping algorithm which quantified by fuzzy logic. Comparative experimental results show that the algorithm can avoid falling into part optimal solution and astringe to the global optimum solution quickly，which has achieved good results in the running time and performance of the solution.

Keywords： quantum encoding quantum genetic algorithm frog leaping algorithm swarm intelligence

HTML PDF Collect

The Application of FDTD and Micro Genetic Algorithms to the Planar Spiral Inductors

Wang Hongjian,Li Jing,Liu Heguang,Jiang Jingshan

Strategic Study of CAE 2004, Volume 6, Issue 11, Pages 38-42

Abstract:

High Q inductors are the important elements for RF circuit design. In this paper, the FDTD method is applied to explain the crowding effect of the spiral inductor , which can never be accurately analyzed by analytical solutions. The experimental results verify the FDTD simulation. The micro genetic algorithms and FDTD are combined to design the high Q inductor. The results show the efficiency of this exploration.

Keywords： FDTD genetic algorithms(GA) spiral inductor quality factor

HTML PDF Collect

Improved dynamic grey wolf optimizer Research Articles

Xiaoqing Zhang, Yuye Zhang, Zhengfeng Ming,249140543@qq.com

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 6, Pages 887-890 doi: 10.1631/FITEE.2000191

Abstract: In the standard (GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic (DGWO1) and the second dynamic (DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.

Keywords：群智能；灰狼优化算法；动态灰狼优化算法；优化实验

HTML PDF Collect

A Parallel Evolutionary Algorithm Based on Space Contraction

Wang Tao,LiQiqiang

Strategic Study of CAE 2003, Volume 5, Issue 3, Pages 57-61

Abstract:

A novel algorithm which is based on space contraction for solving MINLP problems is proposed. The algorithm applies fast and effective non-complete evolution to the search for the information of better solutions, by which locates the possible area of optimal solutions, determines next search space by the information of elite individuals. The result shows that it is better than other existing evolutionary algorithms in search efficiency, range of applications, accuracy and robustness of solutions.

Keywords： space contraction evolutionary algorithms MINLP

HTML PDF Collect

Survey of the Algorithms on Association Rule Mining

Bi Jianxin,Zhang Qishan

Strategic Study of CAE 2005, Volume 7, Issue 4, Pages 88-94

Abstract:

In this paper the principle of the algorithms on association rule mining is introduced firstly, and researches of the algorithms on association rule mining are summarized in turn according to variable (dimension), abstract levels data and types of transacted variable (Boolean and Quantitative) in the process of data mining. At the same time some typical algorithms are analyzed and compared. At last, some future directions on association rule generation are viewed.

Keywords： data mining association rule algorithms survey

HTML PDF Collect

TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data None

Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, Ali Mustafa QAMAR

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6, Pages 763-782 doi: 10.1631/FITEE.1700517

Abstract: Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution (TIE) algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.

Keywords： Taxonomy Clustering algorithms Information science Knowledge management Machine learning

HTML PDF Collect

United Algorithm for Dynamic Subcarrier, Bit and Power Allocation in OFDM System

Gao Huanqin,Feng Guangzeng,Zhuqi

Strategic Study of CAE 2006, Volume 8, Issue 3, Pages 62-65

Abstract:

A realtime united algorithm for dynamic subbcarrier, bit and power allocation according to the change of channel (UA) is presented in this paper, which can be used into the down-link of multi-user orthogonal frequency division multiplexing (OFDM) system. With the algorithm the total transmission power is the minimum while the data rate of each user and the required BER performance can be achieved. Comparing to the subcarrier allocation algorithm (WSA) , the simulation results show that the algorithm presented in this paper has better performance while both have equal calculating complexity.

Keywords： OFDM Wong's subcarrier allocation (WSA) UA

HTML PDF Collect

Modified Binary Artificial Bee Colony Algorithm forMultidimensional Knapsack Problem

Wang Zhigang,Xia Huiming

Strategic Study of CAE 2014, Volume 16, Issue 8, Pages 106-112

Abstract:

The binary artificial bee colony algorithm has the shortcomings of slower convergence speed and falling into local optimum easily. According to the defects, a modified binary artificial bee colony algorithm is proposed. The algorithm redesign neighborhood search formula in artificial bee colony algorithm, the probability of the food position depends on the Bayes formula. The modified algorithm was used for solving multidimensional knapsack problem, during the evolution process, it uses the greedy algorithm repairs the infeasible solution and rectify knapsack resources with insufficient use. The simulation results show the feasibility and effectiveness of the proposed algorithm.

Keywords： artificial bee colony algorithm multidimensional knapsack problem greedy algorithm combinatorial optimization

HTML PDF Collect

Short-term Load Forecasting Using Neural Network

Luo Mei

Strategic Study of CAE 2007, Volume 9, Issue 5, Pages 77-80

Abstract:

Based on the load data of meritorious power of some area power system, three BP ANN models, namely SDBP, LMBP and BRBP Model, are established to carry out the short-term load forecasting work, and the results are compared. Since the traditional BP algorithm has some unavoidable disadvantages, such as the low training speed and the possibility of being plunged into minimums local minimizing the optimized function, an optimized L-M algorithm, which can accelerate the training of neural network and improve the stability of the convergence, should be applied to forecast to reduce the mean relative error. Bayesian regularization can overcome the over fitting and improve the generalization of ANN.

Keywords： short-term load forecasting(STLF) ANN Levenberg-Marquardt Bayesian regularization optimized algorithms