A new adaptive mutative scale chaos optimization algorithm and its application

Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.     

一类推广割线法的特性研究及其分形图

Newton's method and generalized Newton's methods are efficient and convenient tools for constructing chaotic fractal images ,and have been widely investigated in recent years. This paper gives a class generalized Newton's methods that come from Secant method ,and constructs their chaotic fractal images to support the analyses of their algorithm.     

新的基于双混沌系统和压缩感知的图像加密算法

Image encryption technology plays an important role in today’s multimedia applications and Internet information security transmission. However, most image encryption algorithms still have problems, for example, cipher images occupied high bandwidth during transmission, image encryption speed is slow, image encryption algorithms are not associated with plain image, and there is no complete ciphertext feedback mechanism. These prob-lems all affect the security and easy use of image encryption algorithms. In order to solve the above problems, the compressive sensing technology and chaotic system were studied, and a new image encryption algorithm based on double chaotic system and compressive sensing with plaintext association was proposed. The plaintext image association key was used to associate the plain image hash value with the Logistic chaotic system parameters. Discrete wavelet transform, was used to sparse the plain image. Then the random measurement matrix was generated by the Logistic chaos system, and the image was encrypted once by combining the compressive sensing technique and the random measurement matrix to obtain the intermediate image. The intermediate image was hashed again, and the Rucklidge chaos system initial value was associated with the plain image hash value together. The Rucklidge chaotic system and encryption algorithm were used to control the intermediate image for secondary encryption and accord-ingly obtain the cipher image. The encryption algorithm was a new plaintext correlation encryption algorithm, which used the image’s own pixel value to control the scrambling of the intermediate image. It also enhanced the plaintext association and established a ciphertext feedback mechanism. Simulation results and performance analysis show that the algorithm has good encryption performance. The encrypted image can be compressed according to the compres-sion ratio, effectively reducing the size of the cipher image. Moreover, it is resistant to common attacks such as known plaintext attack, selective plaintext attack and differential attack, which is better than other common image encryption algorithms. © 2022, Beijing Xintong Media Co., Ltd.. All rights reserved.     

Chaos block cipher for wireless sensor network

New block cipher algorithm in single byte for wireless sensor network with excellence of many cipher algorithms is studied. The child keys are generated through the developed discrete Logistic mapping, and the Feistel encrypting function with discrete chaos operation is constructed. The single byte block is encrypted and decrypted through one turn permutation, being divided into two semi-byte, quadri- Feistel structural operation, and one turn permutation again. The amount of keys may be variable with the turns of Feistel structural operation. The random and security of the child key was proven, and the experiment for the block cipher in wireless sensor network was completed. The result indicates that the algorithm is more secure and the chaos block cipher in single byte is feasible for wireless sensor network.     

基于绝对贪心和预期效率的0-1背包问题优化

With analysis and research the traditional theory of solving knapsack problem, and then to optimize enigmatical knapsack problems, this paper proposed a new algorithm based on the absolute greedy and expected efficiency strategy. Through the three sets of simulation experiments, it shows that the algorithm can solve a class of knapsack problems and it is superior to greedy algorithm, backtracking algorithm, dynamic programming algorithm, branch and bound algorithm. The convergence speed is ten times as the artificial glowworm swam algorithm by comparing with these two algorithms. Finally, it analyzed discrete degree of data and determined an adaptive scope of the algorithm.     

Unified Approach for Developing EfficientAlgorithmic Programs

A unified approach called partition-and-recur for developing efficient and correct algorithmic programs is presented.An algorithm(represented by recurrence and initiation)is separated from program,and special attention is paid to algorithm manipulation rather than proram calculus.An algorithm is exactly a set of mathematical formulae.It is easier for formal erivation and proof.After getting efficient and correct algorithm,a trivial transformation is used to get a final rogram,The approach covers several known algorithm design techniques,e.g.dynamic programming,greedy,divide-and-conquer and enumeration,etc.The techniques of partition and recurrence are not new.Partition is a general approach for dealing with complicated objects and is typically used in divide-and-conquer approach.Recurrence is used in algorithm analysis,in developing loop invariants and dynamic programming approach.The main contribution is combining two techniques used in typical algorithm development into a unified and systematic approach to develop general efficient algorithmic programs and presenting a new representation of algorithm that is easier for understanding and demonstrating the correctness and ingenuity of algorithmicprograms.     

The Study of Otsu Algorithm Applied in the Measuring of Ash Proportion

To further improve the boiler ash ratio detection methods and resource utilization, through image processing technology for boiler ash ratio analysis, the article first studied the one-dimensional Otsu algorithm, and then for the one-dimensional Otsu algorithm, in order to improve the accuracy of the algorithm, then it puts forward a two-dimensional Otsu algorithm. Finally the two-dimensional Otsu algorithm combined with the one-dimensional Otsu algorithm and the improved Otsu algorithm. By analyzing the improved Otsu algorithm, this paper considers the pixel gray value, neighborhood information, excluding light, noise and the relative efficiency of one-dimensional Otsu algorithm higher accuracy. The relative dimensional Otsu algorithm operating efficiency has been greatly improved. Improved Otsu algorithm in dealing with boiler ash ratio detection has played a very good part in the ecological environment, economic development and some other important aspects.     

NNF and NNPrF—Fuzzy Petri Nets Based on Neural Network for Knowledge Representation, Reasoning and Learning

This paper proposes NNF-a fuzzy Petri Net system based on neural network for proposition logic repesentation,and gives the formal definition of NNF.For the NNF model,forward reasoning algorithm,backward reasoning algorithm and knowledge learning algorithm are discussed based on weight training algorithm of neural network-Back Propagation algorithm.Thus NNF is endowed with the ability of learning a rule.The paper concludes with a discussion on extending NNF to predicate logic,forming NNPrF,and proposing the formal definition and a reasoning algorithm of NNPrF.     

HPSO-based fuzzy neural network control for AUV

A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.     

自适应多位变异遗传算法的实现

Genetic algorithm is a widely used optimization method. Crossover and mutation are two Basicl operatorsof the genetic algorithm. On the basis of analyzing the principles of simple genetic algorithm and discussing its exist-ing problems of crossover point and mutation bit, this paper presents a way of the adaptive multiple bit mutation ge-netic algorithm , which not only can keep the population diversity but also has quicker convergence speed. The resultsof the multi-modal function optimization show that the adaptive multiple bit mutation genetic algorithm is practical and efficient.     

混沌免疫优化组合算法

利用混沌迭代的遍历性和内在随机性。提出一种混沌免疫优化组合算法．该算法综合了免疫进化算法和混沌优化算法各自的空间搜索优势，将混沌变量加载于免疫算法的变量群体．利用混沌搜索的特点对记忆库群体进行微小扰动，并逐步调整扰动幅度．实验结果表明，该算法能明显改善免疫进化算法的收敛性能，搜索效率也得到了显著提高．     

基于混沌序列的图像加密解密算法

提出了基于混沌序列的图像加密解密算法，该算法由一种新的混沌系统生成整数值混沌序列，然后利用该整数值混沌序列把图像的每一像素变换到另一位置，得到加密图像。该算法具有计算量少、安全性高、无失真的特点，实验结果令人满意。     

改进的混沌优化方法及其应用

提出一种改进的混沌优化方法，该方法利用混沌变量对当前点进行扰动，并且通过时变参数逐渐减小搜索进程中的扰动幅度，同时以一定方式确定了时变参数的初值。用改进后的方法对连续对象的全局优化问题进行优化，仿真结果表明，该方法可以显著提高收敛速度和精确性。     

混沌在Hopfield联想记忆网络中的应用

将混沌应用到Hofield联想记忆网络中,利用混沌的遍历性和随机性等独特的性质,可以使待联想模式跳出伪模式的吸引域,而到达存储模式的吸引域内,从而解决了Hopfield网络在噪信比较高的情况下,联想成功率较低的问题。仿真结果证明了该方法的有效性。     

一种彩色图像的混沌加密算法

提出了一种彩色图像的混沌加密算法,利用优化后的高维Lorenz混沌映射对图像置乱来实现对彩色图像的加密.实验证明,该算法实现简单、安全性高、具有良好的加密效果.     

一种基于混沌变异的双群进化规划算法

易早熟收敛是传统进化规划算法的致命缺点。该文在分析了导致进化规划算法早熟原因的基础上，提出了一种基于混沌变异的多群进化规划算法。在该算法中，进化在两个并行的子群间同时进行，一个种群使用混沌变异算子对解空间进行充分的探索，另一个种群使用指数衰减的高斯变异算子对局部进行详尽搜索，种群问的信息交换通过种群的个体交流实现。对典型算例的数值仿真表明，该算法能够改善传统的进化规划算法易早熟收敛的弱点，同时具有良好的快速收敛性和参数鲁棒性。     

一种基于混沌理论的图像加密算法

提出了基于LDC复合混沌的图像加密算法,算法通过利用Lorenz、Duffing2和Chens三个三维混沌系统产生三个复合混沌系统来提高混沌系统的安全性,并对三个混沌系统都进行了改进,使其产生的混沌序列具有0,1分布均匀和较好的图像直方图特性。     

基于混沌搜索的粒子群优化算法

粒子群优化算法(PSO)是一种有效的随机全局优化技术。文章把混沌优化搜索技术引入到PSO算法中,提出了基于混沌搜索的粒子群优化算法。该算法保持了PSO算法结构简单的特点,改善了PSO算法的全局寻优能力,提高的算法的收敛速度和计算精度。仿真计算表明,该算法的性能优于基本PSO算法。     

一种基于神经网络的多级混沌加密算法

混沌信号具有良好的伪随机性，可用于构成加密系统。提出了一种基于多级混沌模型进行加密的算法，并在此基础上利用混沌对误差的极端敏感性，通过神经网络对误差反向传播，改进算法，使其具有更高的保密性。实验表明该算法安全性好，易于实现。     

基于Baker映射的混沌图像加密算法

提出了一种基于二维Baker映射的混沌图像加密算法,通过图像置乱和像素值变换实现了图像的加密.实验证明,该算法密钥空间大,安全性强,能够实现对任意大小图像的加密,具有良好的加密效果.