Chaos Algorithm Research Articles

Exponential slime mould algorithm based spatial arrays optimization of hybrid wind-wave-PV systems for power enhancement

Renewable clean energy sources, such as wind, solar, and wave energy, are currently gaining prominence and are being extensively researched. Given the escalating significance of clean energy, hybrid systems have gradually become an effective solution to address energy and environmental issues. In order to maximize the advantages of wind, wave energy, and photovoltaic (PV), this paper proposes a hybrid wind-wave-PV system (HWWPS) by combining wind turbines, PV panels, and wave energy converter (WEC) to achieve higher energy production and efficiency. To further enhance power output, this paper focuses on the influence of system spatial array optimization on power output and adopts an algorithm to establish a strategic layout. Through the integration of the chaos algorithm, exponential asynchronous factor, and the sine-cosine mechanism, the original slime mould algorithm (SMA) algorithm is enhanced to the exponential slime mould algorithm (ESMA), which has better optimization capability and is particularly suitable for determining the optimal power configuration. Simulations are conducted on hybrid systems consisting of three, seven, and thirteen buoys, respectively, which compare the ESMA are compared with the other six algorithms. The results verify the advantages of ESMA over the other algorithms, and demonstrates the superiority becomes more prominent as the system size increases.

Planar array sparse distribution based on T-2GWO algorithm

Abstract This paper introduces an approach termed T-2GWO (Tent chaos-dual channel Grey Wolf Optimization) algorithm designed for achieving sparse distribution within constrained position ranges for planar arrays. The initial population is generated by using the Tent Chaos algorithm, modeling individual coordinate positions within the population as a two-channel matrix. Subsequently, the Grey Wolf Algorithm is employed for optimization purposes. The algorithm has verified its effectiveness under the assumption of uniform excitation and can obtain the minimum peak side lobe level, reaching peak side lobe levels of -25.78 dB and - 26.15 dB in the azimuth and pitch directions. Compared with the genetic algorithm, the reduction is 3.53 dB and 10.32 dB respectively. At the same time, better main beam radiation can be obtained.

Novel Numerical Solutions to the Fractional-Stochastic Systems

Fractional-stochastic differential equations are widely used tools to simulate a wide range of engineering and scientific phenomena. In this paper, the applicability of the approach of indeterminate coefficients to various fractional-stochastic models is examined. These models have a fractional white noise term and are mostly produced by fractional-order derivative operators. We also look into the application of a polynomial chaos algorithm to stochastic Lotka-Volterra and Benney systems. Fractional-stochastic equations are entirely novel systems that have the potential to function as models for a wide range of scientific and engineering phenomena. It is noted that fractional-order systems with uncertainty or a noise term can benefit from the effective use of Galerkin type approaches used in this article. Keywords: Galerkin method, Numerical solutions, Stochastic-fractional systems

Enhancing copyright protection with Hadamard transform-based blind digital watermarking using the cycling chaos system

The network's ease of use has brought a copyright dilemma for some multimedia works. It is consequently necessary to develop a safe digital watermarking technology to guarantee the protection of digital work's copyright. The proposed method addresses the pressing need for robust blind digital watermarking technology to safeguard the copyrights of multimedia works in the face of the accessibility provided by modern networks. Two approaches are presented in our research paper. First, a blind Hadamard transform-based method is proposed, leveraging the energy concentration concept inherent in the transform. This technique embeds watermarks into the highest energy coefficients of color host images in the transform domain. Notably, watermark extraction is achieved without accessing the original data. Extensive simulation tests validate the efficacy of this method. Second, we have explored the burgeoning field of chaotic systems in watermarking. An LSB-based strategy, termed the cycling chaos algorithm, is introduced. This approach seamlessly integrates data, into color images using the least significant bits. It employs a cycling chaos function to generate a key for a pseudorandom number generator, which, in turn, determines the intensity and pixel positions for storing privileged data in the original image. Through simulation trials, the significant impact of this method on various performance metrics is demonstrated. These contributions aim to not only address immediate copyright protection challenges but also inspire further exploration and advancement in digital watermarking.

Retracted: Multimedia Image Encryption Analysis Based on High-Dimensional Chaos Algorithm

Retracted: Multimedia Image Encryption Analysis Based on High-Dimensional Chaos Algorithm

Security of Encrypted Images in Network Transmission Based on an Improved Chaos Algorithm

With the wide application of 5G networks, many digital images must rely on networks for transmission. Traditional image encryption algorithms can no longer meet modern security requirements, and it is important to protect digital images in network transmission more securely. To address the shortcomings of traditional chaotic algorithms in image encryption, such as the strong randomness of image pixel replacement and time-consuming computations of image pixel iteration, we use a fractional-order Fourier transform to replace the image pixel matrix, a one-dimensional logistic chaos algorithm to reduce the problem of strong randomness of image pixels, and a sine chaos-based idea to optimize the diffusion algorithm to reduce the computational complexity. After encrypting a digital image in simulation experiments, we achieved better results through statistical analysis, adjacent pixel correlation, and resistance to differential attack performance analysis index tests, and verified the protection effect of this algorithm in digital images during network attacks.

Robust visual tracking based on modified mayfly optimization algorithm

In this study, MOA is applied to visual target tracking for the first time, and a novel meta-heuristic tracking algorithm with efficiency and precision is obtained. Similar to the common problems of other classical swarm intelligence algorithms, standard MOA faces a high probability of falling into local extremals, early maturity and a low efficiency of a late convergence speed. Therefore, super-MOA, a modified MOA method, is proposed in this study. By designing the updating mechanism, the position and velocity parameters of ephemera are monitored and dynamically adjusted with the iteration degree, and the balance of the global and local optimization process in different iteration stages is improved. A mayfly progeny mutation strategy was proposed to alleviate the prematurity problem of the standard MOA algorithm. Meanwhile, we introduce a chaos algorithm to reconstruct the velocity parameter iteration mechanism, which alleviates the efficiency loss caused by the frequent repeated searching of historical locations by mayflies in standard MOA. In addition, we further understand and improve the parameters such as the dynamic gravitational coefficient and dance coefficient during the courtship flight of mayflies. We also design a frame size adaptive adjustment strategy, which effectively decreases the interference of invalid features. In terms of experiments, we compare this algorithm with other classical trackers from the qualitative, quantitative and statistical perspectives through OTB2015, VOT2018 and typical large-scale benchmarks. Sufficient tracking experiments in various tracking scenes show that our tracker performs great in terms of efficiency, robustness and accuracy.

Efficient dynamic channel assignment through laser chaos: a multiuser parallel processing learning algorithm

As laser chaos has been proven to be a robust tool to solve the multi-armed bandit (MAB) problem, this study investigates the problem of multiuser dynamic channel assignment using laser chaos in cognitive radio networks with K-orthogonal channels and M secondary users. A novel dynamic channel assignment algorithm with laser chaos series for multiple users, named parallel processing learning with laser chaos (PPL-LC) algorithm, is proposed to efficiently address two main objectives: stable channel assignment and fuzzy stable channel assignment. The latter objective accounts for the realistic scenario where users have fuzzy preferences and do not necessarily pursue the best preference. The PPL-LC algorithm uses the randomness properties of laser chaos to learn the assignment of channels to multiple users without any limitations on the number of channels, which has not been considered in existing laser chaos algorithms. Moreover, the PPL-LC is equipped with parallel processing channel selections, resulting in higher throughput and stronger adaptability with environmental changes over time than comparison algorithms, such as distributed stable strategy learning and coordinated stable marriage MAB algorithms. Finally, numerical examples are presented to demonstrate the performance of the PPL-LC algorithm.

Achieving Light-Weighted Secure Scheme for Communication in a Smart Grid

There are many security risks in the communication between the various nodes of the smart grid. Among these security risks, data leakage is a common one, which occurs in communicating between any two smart meters. In the existing studies, the data leakage is tackled by proposing various security strategies. In this paper, we propose a novel security communication scheme to ensure the security of transmission data between two smart meters against external malicious attacks. Specifically, an authentication scheme based on chaos theory is proposed for verifying the identity trustworthiness of smart meters. In the authentication phase, we use a chaos algorithm and a hash function to compute a shared session key that is used for authentication of both communicating parties, as well as for encryption of private information. Our proposed scheme implements a one-time session approach, i.e., each round of communication keys are unrelated to each other. In relevant validation experiments, we verify that the chaotic algorithm is sufficiently time efficient and compare the computational efficiency with other literature in terms of communication expenses, and the results prove that the proposed scheme is of lightweight nature. Finally we use the Proverif formal verification tool the BAN logic to verify the security as well as the feasibility of the proposed scheme.

Dynamic Random Graph Protection Scheme Based on Chaos and Cryptographic Random Mapping

Advances in network technology have enhanced the concern for network security issues. In order to address the problem that hopping graph are vulnerable to external attacks (e.g., the changing rules of fixed graphs are more easily grasped by attackers) and the challenge of achieving both interactivity and randomness in a network environment, this paper proposed a scheme for a dynamic graph based on chaos and cryptographic random mapping. The scheme allows hopping nodes to compute and obtain dynamically random and uncorrelated graph of other nodes independently of each other without additional interaction after the computational process of synchronous mirroring. We first iterate through the chaos algorithm to generate random seed parameters, which are used as input parameters for the encryption algorithm; secondly, we execute the encryption algorithm to generate a ciphertext of a specified length, which is converted into a fixed point number; and finally, the fixed point number is mapped to the network parameters corresponding to each node. The hopping nodes are independently updated with the same hopping map at each hopping period, and the configuration of their own network parameters is updated, so that the updated graph can effectively prevent external attacks. Finally, we have carried out simulation experiments and related tests on the proposed scheme and demonstrated that the performance requirements of the random graphs can be satisfied in both general and extreme cases.

A multistrategy hybrid adaptive whale optimization algorithm

Abstract To solve the problems of whale optimization algorithm (WOA) with slow convergence speed, low accuracy, and poor global search ability, a multistrategy hybrid adaptive whale optimization algorithm (MHWOA) was proposed. In this algorithm, the logistic–tent hybrid chaos algorithm was used to initialize the population, which could make the population distribution more random and uniform. The opposition-based learning strategy was adopted to expand the population of individuals and complete the population selection. To balance the exploitation phase and exploration phase, a dynamic parameter was constructed based on the sigmoid excitation function, and an active adaptive weight was added to adjust the global and local search, which accelerated the convergence speed also. The perturbation mechanism of the Student T-distribution was introduced with random perturbation to expand the search range and improve the global search ability of the algorithm. In total, 23 benchmark functions were selected to conduct convergence performance and optimization performance experiments of the proposed algorithm. The average value and standard deviation were determined as evaluation indexes. The MHWOA was compared with other improved WOA variants and advanced algorithms. The results showed that the proposed MHWOA had better iterative convergence and optimization performance than different algorithms on the optimization of unimodal functions, multimodal functions, and fixed dimension functions. Meanwhile, the MHWOA was applied to the optimal designs of pressure vessels and springs. The experimental results displayed that the MHWOA obtained better solutions than other meta-heuristic algorithms. This study has practical solid application value, which can be applied to solving various engineering problems.

Improved Chaotic Algorithm-Based Optimization Technology of Architectural Engineering Drawing Parameters.

The traditional methods deal with large sample data sets of architectural engineering drawings and they have high time complexity and space complexity as well. Their searching time is long and sometimes the results are unsatisfactory. Therefore, this paper proposes an optimization method designed for architectural engineering drawing parameters to overcome the limitations of the traditional methods. It is based on the improved chaotic algorithm. The algorithm proposes the optimization model of architectural engineering drawing (AED) parameters in the first phase. In the second phase, an improved chaos algorithm is used to optimize the parameters of architectural engineering drawing, and the modeling strategy of visual parameter optimization environment is constructed. Finally, the visualization parameter optimization process of architectural engineering drawing is completed. Through experiments, it is evidently observed that the method presented in this paper can effectively reduce the optimization time, improve the lighting illumination of buildings, and improve the optimization precision of architectural engineering drawing parameters. The proposed method considers multiple parameters and it has greater application ability in the field of architectural designs.

Multi-Image Encryption Method via Computational Integral Imaging Algorithm.

Under the framework of computational integral imaging, a multi-image encryption scheme based on the DNA-chaos algorithm is proposed. In this scheme, multiple images are merged to one image by a computational integral imaging algorithm, which significantly improves the efficiency of image encryption. Meanwhile, the computational integral imaging algorithm can merge images at different depth distances, thereby the different depth distances of multiple images can also be used as keys to increase the security of the encryption method. In addition, the high randomness of the chaos algorithm is combined to address the outline effect caused by the DNA encryption algorithm. We have experimentally verified the proposed multi-image encryption scheme. The entropy value of the encrypted image is , whereas the entropy value of the merge image with two input images is , which greatly reduces the relevance of the image. The simulation results also confirm that the proposed encryption scheme has high key security and can protect against various attacks.

A New Conformal Map for Polynomial Chaos Applied to Direction-of-Arrival Estimation via UCA Root-MUSIC.

The effects of random array deformations on Direction-of-Arrival (DOA) estimation with root-Multiple Signal Classification for uniform circular arrays (UCA root-MUSIC) are characterized by a conformally mapped generalized Polynomial Chaos (gPC) algorithm. The studied random deformations of the array are elliptical and are described by different Beta distributions. To successfully capture the erratic deviations in DOA estimates that occur at larger deformations, specifically at the edges of the distributions, a novel conformal map is introduced, based on the hyperbolic tangent function. The application of this new map is compared to regular gPC and Monte Carlo sampling as a reference. A significant increase in convergence rate is observed. The numerical experiments show that the UCA root-MUSIC algorithm is robust to the considered array deformations, since the resulting errors on the DOA estimates are limited to only 2 to 3 degrees in most cases.

Construction of a Prediction Model for College Students' Psychological Disorders Based on Decision Systems and Improved Neural Networks.

Modeling and prediction of psychological disorders is a hot topic in current research. Neural networks are very important factors in improving the accuracy and precision ratios of the models which are developed for the prediction of the psychological disorders. An upgraded neural network prediction model of psychological diseases was suggested in order to attain an optimum prediction effect of psychological disorders. First, it analyzes the current progress in predicting the psychological barrier, finds the current limitations of various psychological barrier forecast model, collects the historical data of psychological barriers, and introduces the chaos algorithm of mental disorder history data preprocessing, psychological barriers to better mining change characteristic, and then, after pretreatment using neural network to the psychological barriers to learning history data, introduce the grain subgroup algorithm to improve the problems existing in the neural network, establish a prediction model of the optimal psychological barriers, and finally, through the contrast test and other psychological obstacle prediction model, the results depict enhanced neural network psychological barrier prediction accuracy of more than 95%, compared with the contrast model. Precision is improved by more than 5%. At the same time, the psychological barrier modeling time is shorter, improving the psychological barriers to predict. The efficiency has a higher practical application value.

Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm.

In order to realize the real-time and accurate prediction of dissolved oxygen concentration in the sewage treatment process, a prediction model of dissolved oxygen concentration in the sewage treatment process based on a data identification algorithm was proposed. Combined with the data characteristics of the sewage treatment process, a new sample similarity measure is defined to extract more representative modeling data. In the improved algorithm, in order to improve the quality of the initial members of the basic fireworks algorithm, the chaos algorithm is integrated. The search mechanism of the basic fireworks algorithm is improved, and the optimization process is divided into two stages based on the set criteria, and two groups are used simultaneously. The results show that compared with the basic FWA algorithm, the CFWA algorithm makes better use of the chaotic search mechanism. On the one hand, it avoids the excessive random or blind selection of the initial weight threshold of the neural network in the initial stage; on the other hand, in the optimization process of the weight threshold, two types of search mechanisms, FWA and COA, are used to give full play to their respective strengths and to continuously conduct information exchange and mutual cooperation between groups and individuals. The number of times is better than the basic FWA algorithm, and the training error and generalization error of the CFWA model in the simulation results of the soft sensor model are also better than those of the FWA model, which fully verifies the effectiveness of the CFWA algorithm. It is proved that the data recognition algorithm can effectively predict sewage treatment. It is proved that the data recognition algorithm can effectively predict the dissolved oxygen concentration in wastewater treatment process. It provides a new measurement method for some key process variables that cannot be measured or are difficult to measure in complex chemical processes.

Optimization and Design of Key Expansion of SM4 Algorithm Based on ZYNQ

As the application of "China Intelligent Manufacturing" in the field of production becomes more and more extensive, it becomes crucial to ensure the security of information interaction between production equipment. China's domestic encryption algorithm - SM4 block encryption algorithm came into being. The algorithm consists of a key expansion function and an encryption and decryption function. The key expansion function collects 128-bit keys at a time, and generates thirty-two 32-bit encryption keys after XOR operation, nonlinear permutation operation (S-box) and linear shift operation.The XOR operation parameter CK of the original key expansion function is obtained by the modulo method, which is similar to the system parameter FK and is a fixed value. In order to reduce the risk of deciphering caused by fixed parameters, the lightweight two-dimensional chaos algorithm Hemon is introduced for optimization design. The system parameters used in the SM4 algorithm are randomly generated with the change of the key, which improves the security of the algorithm. And use the hardware encryption platform ZYNQ-7000 series model xc7z100ffv900-1 FPGA side design and implementation, relying on the hard to decipher of hardware encryption to complete the optimization of SM4 algorithm key exchange.

Model Analysis of Applying Computer Monitoring to College Students’ Mental Health

In the past 20 years, although there are many achievements in the model analysis and research, there are still problems of low data utilization and low accuracy. This paper analyzes the mental health level of college students based on chaotic algorithm. At the same time, the application of computer monitoring algorithm to students’ real life psychology is discussed. According to different types of mental health analysis models, the high-precision matching analysis of different students is realized. At the same time, according to the personality characteristics and psychological changes of different students, the model is established and analyzed. Finally, an experiment is designed to carry out practical application and data analysis of the mental health analysis model. The results show that the intelligent analysis model based on computer chaos algorithm has better classification effect. In addition, the algorithm can also make different evaluation strategies according to the different personality of students and can carry out multidimensional classification for college students of different majors. It has effectively increased the proportion of college students’ mental health groups. Compared with the current mainstream algorithms, the algorithm used in this study can adaptively classify college students of different majors. The accuracy of the experimental results is improved by at least 37% compared with the traditional method, and the error is low.

Fibonacci Multichaos Algorithm for Medical Image Encryption for Transmission Through Wavelet Transform Based OFDM System and Its VLSI Realization

At every stage of the digital media transfer, storage, and retrieval process, sensitive images are rendered indecipherable through the use of image encryption. Germany's hospitals have relied on image encryption for years to keep patient data from being accessed by the IT personnel, stolen or left recorded into the network system that could be compromised remotely.
 In order to encrypt a file manually, the process is extremely time- and effort-consuming. Automating this process while retaining high security is desirable. In this research, we present an approach for encrypting medical images that combines scientific computing with cryptography.
 This algorithm uses the Fibonacci Multi Chaos Algorithm to encrypt medical images, making them more secure. Distinct wavelet transform orthogonal frequency division multiplexing is used to convey picture data in this work, which is a reliable and secure method.

Research on degradation prediction of rolling bearing based on adaptive multi-GA-BP

Rolling bearings are widely used in industrial equipment. It is of great significance to study the degradation trend of rolling bearings. In this paper, an Adaptive Multi-population Genetic Algorithm (AMGA) is proposed. Firstly, Kernel Principal Component Analysis (KPCA) method is used to fuse the vibration signal in both time domain and frequency domain, which uses kernel to map the sample space to higher dimensional space and uses the higher dimensional space for linear dimensionality reduction. It can effectively reduce the dimension of nonlinear correlation variables and obtain the trend signal representing the Remaining Useful Life (RUL) characteristics of rolling bearing. Moreover, AMGA is proposed to optimize the number of neurons, initial weight, and initial threshold of the Back Propagation (BP) neural network prediction model. AMGA applies chaos algorithm to Genetic Algorithm (GA) to improve the diversity of the initial population. Meanwhile, the communication frequency between different populations is controlled by judging the similarity of the optimal solution among different populations, so as to effectively jump out of the local optimum and obtain the global optimal solution. Finally, the whole life data of the spin-up process of the rolling bearing from University of Cincinnati is taken as an example to analyze the performance of the algorithm. Compared with the traditional BP, the R2-score performance and the MAPE performance of KPCA-AMGA-BP are improved by 0.297 and 2.46% respectively. Furthermore, compared with the optimized BP, this method obtains the improved R2-score performance and the MAPE performance by 0.218 and 0.46%.