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Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments Research

Yan Shao, Zhi-feng Zhao, Rong-peng Li, Yu-geng Zhou,shaoy@zju.edu.cn,zhaozf@zhejianglab.com,lirongpeng@zju.edu.cn,yugeng.zhou@wfjyjt.com

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 5, Pages 649-808 doi: 10.1631/FITEE.1900659

Abstract: Coordinating multiple unmanned aerial vehicles (multi-UAVs) is a challenging technique in highly dynamic and sophisticated environments. Based on as well as current mainstream unmanned system controlling algorithms, we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge. In particular, we put forward a more reasonable and effective pheromone update mechanism, by improving digital pheromone fusion algorithms for different semantic pheromones and planning individuals’ probabilistic behavioral decision-making schemes. Also, inspired by the flocking model in nature, considering the limitations of some individuals in perception and communication, we design a model on top of Olfati-Saber’s algorithm for flocking control, by further replacing the pheromone scalar to a vector. Simulation results show that the proposed algorithm can yield superior performance in terms of coverage, detection and revisit efficiency, and the capability of obstacle avoidance.

Keywords：群体智能；数字信息素；人工势场；领航算法

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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

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Leader-following consensus of second-order nonlinear multi-agent systems subject to disturbances None

Mao-bin LU, Lu LIU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 1, Pages 88-94 doi: 10.1631/FITEE.1800611

Abstract:

In this study, we investigate the leader-following consensus problem of a class of heterogeneous secondorder nonlinear multi-agent systems subject to disturbances. In particular, the nonlinear systems contain uncertainties that can be linearly parameterized. We propose a class of novel distributed control laws, which depends on the relative state of the system and thus can be implemented even when no communication among agents exists. By Barbalat’s lemma, we demonstrate that consensus of the second-order nonlinear multi-agent system can be achieved by the proposed distributed control law. The effectiveness of the main result is verified by its application to consensus control of a group of Van der Pol oscillators.

Keywords： Multi-agent systems Leader-following consensus Distributed control

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Strategic Study of CAE 2004, Volume 6, Issue 5, Pages 87-94

Abstract:

Particle swarm optimization (PSO) is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. PSO can be implemented with ease and few parameters need to be tuned. It has been successfully applied in many areas. In this paper, the basic principles of PSO are introduced at length, and various improvements and applications of PSO are also presented. Finally, some future research directions about PSO are proposed.

Keywords： swarm intelligence evolutionary algorithm particle swarm optimization

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The Optimization of the Monopulse Antenna Based on Genetic Algorithms

Wang Hongjian,Gao Benqing,Liu Ruixiang

Strategic Study of CAE 2002, Volume 4, Issue 5, Pages 84-87

Abstract:

The sum and difference patterns as well as the directivity of the monopulse antenna arrays are optimized based on genetic algorithms (GA). When only the sum pattern is optimized as usual, the difference pattern and gain can not be guaranteed, so the track precision and effective distance of the antenna or missile will be impaired. However, using GA, the sum pattern, difference pattern and directivity can be optimized thoroughly for the antenna design.

Keywords： monopulse antenna array genetic algorithm pattern directivity