Chaotic Logistic Map Research Articles

Temperature Control Strategy for Hydrogen Fuel Cell Based on IPSO-Fuzzy-PID

Hydrogen fuel cell water-thermal management systems suffer from slow response time, system vibration, and large temperature fluctuations of load current changes. In this paper, Logistic chaotic mapping, adaptively adjusted inertia weight and asymmetric learning factors are integrated to enhance the particle swarm optimization (PSO) algorithm and combine it with fuzzy control to propose an innovative improved particle swarm optimization-Fuzzy control strategy. The use of chaotic mapping to initialize the particle population effectively enhances the variety within the population, which subsequently improves the ability to search globally and prevents the algorithm from converging to a local optimum solution prematurely; by improving the parameters of learning coefficients and inertia weight, the global and local search abilities are balanced at different stages of the algorithm, so as to strengthen the algorithm’s convergence certainty while reducing the dependency on expert experience in fuzzy control. In this article, a fuel cell experimental platform is constructed to confirm the validity and efficiency of the recommended strategy, and the analysis reveals that the improved particle swarm optimization (IPSO) algorithm demonstrates better convergence performance than the standard PSO algorithm. The IPSO-Fuzzy-PID management approach is capable of providing a swift response and significantly diminishing the overshoot in the system’s performance, to maintain the system’s safe and stable execution.

An Enhanced Crowned Porcupine Optimization Algorithm Based on Multiple Improvement Strategies

The Crowned Porcupine Optimization (CPO) algorithm exhibits certain deficiencies in initialization efficiency, convergence speed, and adaptability. To address these issues, this paper proposes an enhanced Crowned Porcupine Optimization algorithm (ICPO) based on multiple improvement strategies. ICPO optimizes the initialization process by introducing Logistic chaotic mapping, thereby expanding the search space. It accelerates convergence through an elite retention strategy and enhances global search capability by integrating stochastic operations, mutation-like operations, and crossover-like operations to increase population diversity. Additionally, adaptive step tuning based on fitness values is employed to comprehensively improve the algorithm’s performance. To verify the effectiveness of ICPO, 23 standard functions were used for a comprehensive evaluation, and its practicality was further validated through optimization of actual engineering design problems. The experimental results demonstrate significant improvements in convergence speed, solution quality, and adaptability with ICPO.

A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization.

Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm's global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mapping and lens imaging reverse learning approaches for population initialization to enhance population diversity; balanced global exploration and local development capabilities through nonlinear parameter processing; and introduced a Weibull flight strategy and triangular parade strategy to optimize individual position updates. Additionally, the Gaussian-Cauchy mutation strategy was employed to improve the algorithm's ability to overcome local optima. The experimental results demonstrate that MSCSO performs well on 65.2% of the test functions in the CEC2005 benchmark test; on the 15 datasets of UCI, MSCSO achieved the best average fitness in 93.3% of the datasets and achieved the fewest feature selections in 86.7% of the datasets while attaining the best average accuracy across 100% of the datasets, significantly outperforming other comparative algorithms.

Binocular camera calibration using a BP algorithm optimized by an improved subtraction-average-based optimizer

The BP algorithm exhibits drawbacks such as reduced precision and extended iteration duration during the process of calibrating a binocular camera system. A technique employing an improved subtraction-average-based optimizer to optimize the BP algorithm (ISABO-BP) is introduced to address these limitations and complete the calibration of the binocular camera. The pixel values of the corner in 2D space and the coordinate values in 3D space are used as the input and output for the BP algorithm, respectively. First, the logistic chaotic mapping is used to initialize the population to increase the diversity of the initial population. The piecewise mapping is employed to generate random values to replace the coefficient values in the individual position update formula, making the population distribution more uniform. The golden sine strategy is adopted to help particles escape from local optima. The ISABO-BP algorithm is utilized to perform the process of calibrating a binocular camera. Then, through numerical experiments, the mean calibration precision for the BP algorithm is 0.1501 and 0.0445 cm for the ISABO-BP algorithm. The calibration precision has been enhanced by 70.4%. The iterative steps of the BP algorithm are 353 epochs, while the ISABO-BP algorithm are 76 epochs, and the iterative speed is increased by nearly four times. It is evident that the ISABO-BP algorithm enhances the calibration precision and expedites the convergence rate. Finally, other intelligent algorithms to optimize the BP algorithm for calibrating a binocular camera are compared, and the superiority of this algorithm is also verified.

Security enhancement for NOMA-PON with 2D cellular automata and Turing pattern cascading scramble aided fixed-point extended logistic chaotic encryption

The non-orthogonal multiple access-passive optical network (NOMA-PON) is facing the dual security threats of primary user interference and unauthorized third-party user eavesdropping, so efficient data security enhancement techniques are crucial. To solve these problems, we propose a fixed-point extended (FE)-logistic chaotic mapping to reduce the computational complexity while employing a two-dimensional (2D) cellular automata (CA) and Turing pattern (TP) cascading scramble (CA-TPCS) encryption algorithm to further improve the sensitivity of the NOMA-PON system. The CA-TPCS consists of 2D-CA dynamic bit encryption and Turing symbol substitution (TSS). By using FE chaos to construct 2D-CA and adopting index sort to extract the TSS matrix, dynamic diffusion of bits and scrambling of a 2D symbol matrix are achieved. To ensure the key privacy, we employ a dual key mechanism, and uplink data is introduced as the private key. To verify the feasibility of the proposed method, a simulation validation is built on a 17.6 Gb/s power division multiplexing-orthogonal frequency division multiplexing (PDM-OFDM) NOMA-PON system transmitted over 25 km standard single mode fiber (SSMF). The results show that the proposed scheme has no effect on the optimal power allocation rate (PAR) values and the values are all 3. Meanwhile, the receiver sensitivity gains of 0.2 and 0.3 dB are obtained for high-power and low-power users after encryption. The ciphertext has good diffusion and statistical properties, and the key space is flexibly controlled by the FE precision f, the length l of the transmitted bit, and the size T of the TP, with the value of 22f+l+T×T. The results show that the proposed scheme is not only very compatible with PDM technology but also can realize the dual defense of internal aggression and external aggression. It has a good application prospect in the future NOMA-PON.

Multi-Search Strategy-based Improved Water Flow Optimizer Algorithm for Cluster Analysis

Clustering is a popular technique that has proven its capability in diverse fields like data analytics, business intelligence, social mining, image recognition, document clustering and bioinformatics. This technique determines the valuable information from a pre-defined set of data and groups similar information into the same cluster. In literature, many algorithms have been presented based on several clustering approaches. It is observed that partitional clustering algorithms are widely popular due to there simplicity and easy implementation such as k-means, k-medoids, and k-harmonic means. However, traditional algorithms suffer from several limitations like being trapped in local optima and depending on the initial solution quality. Heuristic algorithms are also proposed to alleviate the problems of traditional clustering algorithms. But, sometimes, these algorithms also get stuck in local minima and exhibit a lack of balance between search mechanisms. Hence, this work presents an improved water flow optimizer (IWFO) algorithm for cluster analysis that can address the issues of traditional and heuristic algorithms. In the proposed IWFO algorithm, the initial solution is generated based on the logistic chaotic map instead of random initialization, and in turn, an optimal quality solution is generated. The search mechanism of the WFO algorithm is enhanced based on an improved search mechanism which is the combination of non-linear functions and the previous best solution. Further, the local optima issue is alleviated using a multi-start search mechanism. The efficacy of the proposed IWFO algorithm is evaluated using twelve benchmark clustering datasets and results are compared with seventeen clustering algorithms. The simulation results are assessed using intra-cluster distance (intra), standard deviation (SD), rank, accuracy rate (AR) and detection rate (DR) parameters. Further, a statistical test is also conducted to validate the efficacy of the proposed IWFO algorithm. The results showed that proposed IWFO algorithms obtain superior quality results on most of the datasets.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

NOVEL TRAIN HEIST OPTIMIZATION AND LOGISTIC CHAOTIC MAP BASED FRESHMAN LEARNING PROCESS INSPIRED ALGORITHM FOR SOLVING THE OPTIMAL POWER FLOW PROBLEM

Train heist based optimization (THO) algorithm is designed based on the really happened train heist in United Kingdom. In the exploration section, robbery gang has been executed the plan has been imitated and mathematically defined. Prior to the execution the robbers plan the conspiracy after multiple examinations on the track. In the exploitation section fled activities of the robbery gang has been emulated and scientifically defined. Even though Leatherslade Farm had been already purchased by the robbery gang, they altered the plan rapidly with minimum stay period in the farm by listening in to the police very high frequencychannels. Chaotic communiqué amongst the gang members and Perception of movement may differ among the gang members and the error value in the decision of the movement is defined. Then in tis paper Logistic chaotic map based freshman learning process inspired optimization (LCP) algorithm is designed to solve the problem. LCP algorithm is designed by imitating the freshman learning process.Trainer will enhance learning processof freshman day by day. Freshman lives in ambiguous world and monetary predicament may originate at dissimilar phases of freshman’s life. Deeds of the freshman learning process are mathematically formulated and population engendered based on the logistic chaos mapping. Trainer will explore the complications and consider a mean point of all complications. Suitable guidance and solutions will be delivered to the freshman by the trainer. Train heist based optimization (THO) algorithm and Logistic chaotic map based freshman learning process inspired optimization (LCP) algorithm are validated in 23 Benchmarking functions and IEEE 30, 354 systems.

A novel approach to simplified and secure message cryptography using chaotic logistic maps and index keys

A novel approach to simplified and secure message cryptography using chaotic logistic maps and index keys

Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application.

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm's optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy's efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms.

An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials

Due to the similar characteristics of chaotic systems and cryptography, public key encryption algorithms based on chaotic systems are worth in-depth research and have high value for the future. Chebyshev polynomials have good properties and are often used in the design of public key algorithms. This paper improves the Bose Multi-Chaotic Public Key Cryptographic Algorithm (BMPKC) by applying Chebyshev polynomials. The proposed algorithm (CMPKC-) introduces the selective coefficient based on the properties of Chebyshev polynomials, allowing the special functions that need to be negotiated in the original system to be freely and randomly chosen as Chebyshev polynomials, and can also be expanded to m levels. The improved cryptographic algorithm also utilizes chaotic hash functions and logistic mapping to generate pseudo-random sequences and overcomes shortcomings of the Bose algorithm by iteratively iterating the selected Chebyshev polynomials based on the number of 0s or 1s in the pseudo-random sequence, thus providing better security. Analysis and software testing results indicate that this algorithm has strong robustness against brute force attacks, achieving a higher attack time for breaking the private key compared to the CEPKC, BMPKC, and CMPKC algorithms. Compared to the CMPKC algorithm, our proposal algorithm achieves better performance in the encryption and decryption phases. Furthermore, we combine this Multi-Chaotic System Key Exchange Protocol with the Advanced Encryption Standard (AES) algorithm, while providing a demonstration, offering more possibilities for practical applications of this system.

Hybrid Block Chaotic Compressive Sensing and Chaotic Scrambling Algorithms for Color Image Encryption System

This paper suggests a new image encryption algorithm based on block compressive sensing (BCS) and chaotic scrambling techniques. With compressive sensing (CS), signals can be sampled much lower than the Nyquist-Shannon rate while preserving their information content. BCS can be used as a one-step process by using a 3D logistic chaotic map, the measurement matrix is used as a secret key for encryption. BCS algorithm used an individual image reconstruction algorithm that leverages l_1norm minimization to promote signal sparsity, and a smoothing operator to enhance image quality. An encrypted image is first decomposed into three sub-images based on the tricolor theory; then, a discrete wavelet transform is used to sparsely process the three decomposed images. A 2D Henon map is used to scramble the compressed image after compression. This process further enhances the security of the system by increasing the complexity of the encryption process. The results showed that the proposed system provides better security and has a lower computational complexity than other methods. Furthermore, the proposed system is resistant to known attacks such as brute force and statistical attacks.

Color Image Encryption Model Based on 3-D Chaotic Logistic Map and JAYA Algorithm

In this research, we have proposed a color image encryption model based on a three-dimensional chaotic logistic map and the JAYA algorithm. The 3-D chaotic logistic map generates three random keys for the color image. Further, the JAYA algorithm is utilized to find the parameter values of a 3-D chaotic logistic map based on the objective function. The JAYA algorithm is chosen because no parameter tuning is required and it has a better convergence rate to find the best solution over others. After generating the random key, exclusive-OR, and pixel-level bits are shuffled to get the final encrypted image. With the typical collection of color images, the simulation assessment was performed. The outcome indicates that the distorted image is entirely noisy. In comparison to best-practice approaches described in the literature, the suggested model outperforms them in terms of entropy while demonstrating poor PSNR and CC.

Image-to-Image Steganography with Josephus Permutation and Least Significant Bit (LSB) 3-3-2 Embedding

In digital image security, the Josephus permutation is widely used in cryptography to enhance randomness. However, its application in steganography is underexplored. This study introduces a novel method integrating the Josephus permutation into the LSB 3-3-2 embedding technique for image steganography. This approach improves the randomness of the keystream generated by the chaotic logistic map, addressing vulnerabilities in basic logistic maps susceptible to steganalysis. Our algorithm is tested on RGB images as secret data, presenting higher complexity compared to grayscale images used in previous studies. Comparative analysis shows that the proposed algorithm offers higher payload capacity while maintaining image quality, outperforming traditional LSB techniques. This research advances the field of image steganography by demonstrating the effectiveness of the Josephus permutation in creating more secure and robust steganographic images.

Message steganography using separate locations and blocks

A novel method of message steganography is introduced to solve the disadvantages of traditional least significant bit (LSB) based methods by dividing the covering-stego image into a secret number of blocks. A chaotic logistic map model was performed using the chaotic parameters and the number of image blocks for generating a chaotic key. This key was then sorted, and the locations of blocks 1 to 8 were used to select the required blocks to be used as covering-stego blocks. The introduced method simplifies the process of message bits hiding and extracting by adopting a batch method of bits hiding and extracting. A comparative analysis was conducted between the outcomes of proposed method and those of prevalent approaches to outline the enhancements in both speed and quality of message steganography.

Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO.

Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

Enhancing biometric authentication security through the novel integration of graph theory encryption and chaotic logistic mapping

Pseudo-random number generation based on spatial chaotic map of Logistic type and its cryptographic application

The pseudo-random number generator (PRNG) based on chaos has been widely used in the fields of digital communication, cryptography and computer simulation. In this paper, we study a new PRNG based on spatial surface chaotic system (SSCS), which is constructed based on coupling mapping lattice (CML) and one-dimensional Logistic chaotic map. To verify the performance of this generator, we study its nonlinear dynamic properties, including the Lyapunov exponent, ergodicity and bifurcation phenomena. Moreover, we analyze the cryptographic properties such as key space, key sensitivity, correlation, histogram and information entropy, while performing NIST SP800-22 test and TestU01 test for the randomness of this system. Theoretical analysis and numerical simulation results show that the PRNG proposed in this paper has good complexity, ergodicity, sensitivity and randomness. Moreover, the key space can increase dynamically with the increase of encrypted data, especially suitable for the protection of big data such as multimedia, which is more advantageous than most existing chaotic mapping. The results of this paper will provide a new idea for the study of chaotic cryptography, and motivate the study of new discrete chaotic models with desirable statistical properties.

Stability prediction of roadway surrounding rock using INGO-RF

In order to more accurately classify the stability of roadway surrounding rock and identify dangerous areas in a timely manner to prevent roadway collapse and other disasters, an INGO-RF roadway surrounding rock stability prediction model is proposed. This model combines the improved Northern Gok algorithm (INGO) and Random Forest (RF) based on the analysis of influencing factors of roadway surrounding rock stability. First, three strategies were employed to enhance the Northern Gob algorithm (NGO): Logistic chaotic mapping, refraction reverse learning, and improved sine and cosine. Subsequently, INGO was utilized to optimize the number of decision trees and the minimum number of leaf nodes for RF species in order to improve the prediction accuracy of RF. Secondly, a data set consisting of 34 groups of roadway surrounding rock data is selected. The input indexes of the model include roof strength, two-wall strength, floor strength, burial depth, roadway pillar width, ratio of direct roof thickness to mining height, and surrounding rock integrity. Meanwhile, surrounding rock stability is considered as the output index. Particle swarm optimization backpropagation neural network (PSO-BPNN), genetic algorithm optimization support vector machine (GA-SVM), Sparrow search algorithm optimization RF (SSA-RF) models were introduced to compare the prediction results with the INGO-RF model, and the results showed that: INGO-RF model has the best performance in the comparison of various performance indicators. Compared with other models, the accuracy rate (Ac) in the test set has increased by 0.12∼0.4, the accuracy rate (Pr) has increased by 0.07∼0.65, and the recall rate (Re) has increased by 0.08∼0.37. The harmonic mean (F1-Score) of the recall rate increased by 0.08∼0.52, the mean absolute error (MAE) decreased by 0.1428∼0.4285, the mean absolute percentage error (MAPE) decreased by 7.15%∼28.57%, and the root mean square error (RMSE) decreased by 0.1565∼0.3779. Finally, the data on surrounding rock conditions of roadways in multiple mining areas in Shanxi Province were collected to test the INGO-RF model. The results indicate that the predicted outcomes closely align with the actual results, demonstrating a certain level of reliability and stability. It shows that it can better meet the practical needs of engineering and avoid the occurrence of mine disasters.

Secure Stego Method to Protect SMS Transmission

A novel method of message steganography will be proposed. The method will use a digital speech file as a covering media. The 32 binary bits of each speech sample will be used for data hiding, using these bits will increase the hiding capacity, and changing them will not much affect the sample value, thus the quality of the stego file will be always high. The proposed method will use a private key to protect the hidden message, the protection will be applied by shuffling the rows of the speech binary matrix using the contents of the generated indices key, The induces key will be generated by running a chaotic logistic map model using the values of the chaotic parameters included in the private key. The proposed method will be tested and implemented using various messages, the quality, sensitivity and speed of the proposed method will be analyzed to prove the enhancements provided by the proposed method.