Chaotic Algorithm Research Articles

Revolutionizing crowd safety: A breakthrough hybrid whale-bat chaotic algorithm (WOABCM) for optimized evacuation simulations

The Whale-Bat Chaotic Algorithm (WOABCM) is a revolutionary tool for optimizing pathfinding navigation in crowd evacuation scenarios. Conventional evacuation plans rely on static routes, causing traffic jams and decreased safety. WOABCM uses the erratic behavior of whales and bats to optimize escape routes in real time, making it particularly useful in large crowd evacuations. The algorithm can adapt to different scenarios, ensuring its continued efficacy across real-world conditions. Iteratively iterating through the evacuation environment, WOABCM iteratively finds the most effective ways for agents to reach exits. In this experiment, the results show notable improvements in evacuation times, with WOABCM determining and directing agents through the shortest and least crowded paths in a room setting and assigning agents to exits in more complicated scenarios. The algorithm's flexibility also allows it to swiftly recompute pathways for agents, avoiding obstructions or congestion points. This groundbreaking approach to crowd evacuation simulation demonstrates how chaos theory-inspired algorithms can be used to solve practical problems

Novel modified Chernobyl disaster optimizer for controlling DC motor

This article presents the modified Chernobyl disaster optimizer (CDO) method for DC motor control to find the optimal proportional integral derivative (PID) settings. DC motors are widely used machinery. DC motors are also simple to use. The detonation of the Chernobyl nuclear reactor core served as the inspiration for the idea and guiding principles of the CDO. CDO has limitations in the stability of exploration and exploitation areas. This research aims to obtain a new balance of exploration and exploitation. This study suggests incorporating the levy flight and chaotic algorithm (CA) techniques to enhance the CDO method. This study was conducted with the MATLAB/Simulink software. A comparative technique, which included the marine predator algorithm (MPA), golden jackal optimization (GJO), and CDO, was utilized to determine the performance of the MCDO method. According to the study’s findings, the MCDO method’s overshoot value outperformed all other approaches.

Competition of Three Chaotic Meta-heuristic Algorithms with Physical Inspiration for Optimal Design of Truss Structures

Chaos maps create a significant improvement in the optimization results of meta-heuristic algorithms by creating a balance between the stages of exploration and exploitation. The optimization algorithms of structures are strongly non-linear and non-convex, having several local optima. Chaotic functions, while creating chaotic jumps, provide the conditions for escaping from local optima to global optima. Most of the meta-heuristic algorithms fall into the trap of local optima and suffer some kind of premature convergence. In this paper, by forming three scenarios, chaos functions can be embedded into the exploration, exploitation or both stages at the same time, and improve the results of meta-heuristic algorithms. The considered algorithms are inspired by physical phenomena, with the possibility of accessing classical and regular relations, the effectiveness of chaos functions in meta-heuristic algorithms are increased. Nowadays, chaotic algorithms are widely utilized by researchers and are considered as a challenging topic. In the present research, the effects of logistic and Gaussian chaos functions on the optimization results of three physically inspired meta-heuristic algorithms are investigated. These algorithms include Chaotic Thermal Exchange Optimization (CTEO), Chaotic Big Bang-Big Crunch (CBB-BC), and Chaotic Tug-of-War Optimization (CTWO).

Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm

The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO.

Research on Chaotic Image Encryption Based on Fibonacci-ILogistic-IHenon

To further improve the security of image encryption, we propose an image encryption algorithm that combines Fibonacci with improved logistic and improved Henon chaotic mapping, i.e., the Fibonacci-ILogistic-IHenon algorithm. The algorithm addresses that the existing logistic chaotic algorithm is controlled by the parameter resulting in an uneven distribution, and the Henon chaotic algorithm may have the problem of periodic orbits. Using the mode-taking and sinusoidal functions for optimization, the output sequence region generated by the logistic chaotic mapping is adjusted to the input of the Henon chaotic mapping, which enables the construction of a new chaotic system (ILogistic-IHenon). In the image encryption implementation, Fibonacci is used to cure the chaos, and ILogistic-IHenon is utilized to generate chaotic sequences to complete the encryption. In the simulation experiments, we choose the improved logistic encryption algorithm, the improved Henon encryption algorithm, and the improved logistic-ent encryption algorithm as the comparison algorithms, and the results illustrate that the Fibonacci-ILogistic-IHenon algorithm has better performance in image encryption statistical analysis, encryption speed, pixel correlation analysis, information entropy analysis, differential attack analysis and other metrics.

A comprehensive evaluation of Marine predator chaotic algorithm for feature selection of COVID-19

A comprehensive evaluation of Marine predator chaotic algorithm for feature selection of COVID-19

Social media user information security encryption method based on chaotic algorithm

Social media user information security encryption method based on chaotic algorithm

Social media user information security encryption method based on Chaotic Algorithm

Social media user information security encryption method based on Chaotic Algorithm

A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.

Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction. The focus is on identifying precursors that occur within 10 min of the onset of abnormal electrical activity during a seizure. However, there are security concerns related to safeguarding patient EEG recordings against unauthorized access and network-based attacks. Therefore, there is an urgent need for an efficient prediction and classification method for encrypted EEG data. This paper presents an effective system for analyzing and recognizing encrypted EEG information using Arnold transform algorithms, chaotic mapping, and convolutional neural networks (CNNs). In this system, the EEG time series from each channel is converted into a 2D spectrogram image, which is then encrypted using chaotic algorithms. The encrypted data is subsequently processed by CNNs coupled with transfer learning (TL) frameworks. To optimize the fusion parameters of the ensemble learning classifiers, a hybridized spoofing optimization method is developed by combining the characteristics of corvid and gregarious-seeking agents. The evaluation of the model's effectiveness yielded the following results: 98.9 ± 0.3% accuracy, 98.2 ± 0.7% sensitivity, 98.6 ± 0.6% specificity, 98.6 ± 0.6% precision, and an F1 measure of 98.9 ± 0.6%. When compared with other state-of-the-art techniques applied to the same dataset, this novel strategy demonstrated one of the most effective seizure detection systems, as evidenced by these results.

Spoofing Traffic Attack Recognition Algorithm for Wireless Communication Networks in a Smart City Based on Improved Machine Learning

Abstract It is difficult to find spoofing traffic attack information for a wireless communication network, which leads to poor performance of spoofing traffic attack identification. Therefore, a spoofing traffic attack recognition algorithm for wireless communication networks based on improved machine learning has been designed. The process of network traffic classification and several common network cheating traffic attacks are analyzed. A chaotic algorithm is used to search and collect wireless communication network data, and Min-Max and z-score are used to standardize the collected data. The risk assessment function of wireless communication network spoofing traffic attack is constructed, and the spoofing traffic attack is preliminarily determined according to the function. The convolutional neural network in machine learning is improved, and the preliminary judgment results are input into the improved convolutional neural network to identify the attack behavior. The experimental results show that the recall rate of this method for wireless communication network spoofing traffic attacks can reach 90.08 % at the highest level, and the identification process takes only 1,763 ms at the lowest level. It can control the false positive rate of attacks below 4.68 % and the false positive rate below 2.00 %, and the identification effect of spoofing traffic attacks is good.

Security enhancement in a cloud environment using a hybrid chaotic algorithm with multifactor verification for user authentication

A hybrid chaotic-based DNA and multifactor authentication strategy are created to improve the protection of the cloud environment. Initially, multimodal data are collected from the data owner then the information is compressed by utilizing the deflate compression approach. The data is then encrypted using hybrid chaotic-based DNA cryptography to increase the security of data. In this hybrid algorithm DNA is used for the key generation and chaotic algorithm is utilized for the encryption process. On the other hand, a multifactor authentication method is created to access data from the cloud to block access by unauthorized users. In that technique, users are requested to enter the registered Password with the generated OTP from the mobile number. Then, the device serial number is another factor to verify the accessing device. Likewise, the user's fingerprint and iris recognition are also validated for accessing the data. The cloud-based data can be accessible following users' successful authentication. The simulation analysis shows that the encryption and decryption time reached for image, string and integer data is 24, 0.065, 37, 0.14, 28 and 0.14 s, respectively, for the cloud security algorithm. The proposed algorithm effectively mitigates space consumption and provides improved data security in a cloud environment.

Investigation into defect image segmentation algorithms for galvanised steel sheets under texture backgrounds

Image segmentation is a significant step in image analysis and computer vision. Many entropy-based approaches have been presented on this topic. Among them, Tsallis entropy is one of the best-performing methods. In this paper, the surface defect images of galvanised steel sheets were studied. A two-dimensional asymmetric Tsallis cross-entropy image segmentation algorithm based on chaotic bee colony algorithm optimisation was used to investigate the segmentation of surface defects under complex texture backgrounds. On the basis of Tsallis entropy threshold segmentation, a more concise expression form was used to define the asymmetric Tsallis cross-entropy in order to reduce the calculation complexity of the algorithm. The chaotic algorithm was combined with the artificial bee colony (ABC) algorithm to construct the chaotic bee colony algorithm, so that the optimal threshold of Tsallis entropy could be quickly identified. The experimental results showed that compared with the maximum Shannon entropy algorithm, the calculation time of this algorithm decreased by about 58% and the threshold value increased by about (26%, 54%). Compared with the two-dimensional Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 35% and about 20% was improved in the g-axis direction only. Compared with the two-dimensional asymmetric Tsallis cross-entropy algorithm, the calculation time of this algorithm decreased by about 30% and the threshold values of the two algorithms were almost the same. The algorithm proposed in this paper can rapidly and effectively segment defect targets, making it a more suitable method for detecting surface defects in factories with a rapid production pace.

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.

Comparison of Three Chaotic Meta-heuristic Algorithms for the Optimal Design of Truss Structures with Frequency Constraints

The main reasons for the success of using chaos maps in meta-heuristic algorithms are fast optimization of non-linear and non-convex problems. One of these cases is the control of the natural frequencies of structures to prevent the destructive and dangerous phenomenon of resonance. Natural frequencies have useful information about the dynamic behavior of structures, and by applying dynamic constraints, a significant improvement is achieved in the optimal design of structural weight. Applying frequency limits with traditional and gradient-based methods is very difficult and time-consuming, and in most cases, the calculation process stops at local optima. Recent research shows that chaos maps play a major role in escaping from local optima and reaching global optima. By combining these maps with meta- heuristic algorithms, while avoiding premature convergence, the access to global optima is accelerated and improved, and the ideal state of balance between the exploration and exploitation stages is realized. Today, chaotic algorithms are widely accepted by researchers and are considered as a challenging topic. In a recent research, six chaotic meta- heuristic algorithms have been investigated for the formation and improvement of results with the optimal design of truss structures. In this part the chaotic algorithms include Chaotic Water Evaporation Optimization (CWEO), Chaotic Tug-of-War Optimization (CTWO) and Chaotic Thermal Exchange Optimization (CTEO) are examined.

A Modified Framework for Image Encryption and Decryption Using Modified Chaotic Algorithms Towards Medical Image Security

A Modified Framework for Image Encryption and Decryption Using Modified Chaotic Algorithms Towards Medical Image Security

Evolutionary-based image encryption using Magic Square Chaotic algorithm and RNA codons truth table

Evolutionary-based image encryption using Magic Square Chaotic algorithm and RNA codons truth table

Energy-aware workflow scheduling in fog computing using a hybrid chaotic algorithm

Energy-aware workflow scheduling in fog computing using a hybrid chaotic 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.

New Model of Feature Selection based Chaotic Firefly Algorithm for Arabic Text Categorization

The dimensionality reduction is a type of problem that appear in the most classification processes. It contains a large number of features; these features may contain unreliable data which may lead the categorization process to unwanted results. Feature selection can be used for reducing dimensionality of datasets and find interesting relevant information. In Arabic language, the number of works applies a meta-heuristic algorithm for feature selection is still limited due to the complex nature of Arabic inflectional and derivational rules as well as its intricate grammatical rules and its rich morphology. This paper proposes a new model for Arabic Feature Selection that combines the chaotic method in the Firefly Algorithm (CFA). The Chaotic Algorithm replaces the attractiveness coefficient in firefly algorithm by the outputs of chaotic application. The enhancement of the new approach involves introducing a novel search strategy which is able to obtain a good ratio between exploitation and exploration abilities of the algorithm. In terms In terms of performance, the experiments of the proposed method are tested using classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and three evaluation measures, including precision, recall, and F-measure. The experimental findings show that the combining of CFA and SVM classifiers outperforms other combinations in terms of precision.

A New hyper chaotic algorithm for energy video communication security

The usage of the cameras in facilities in the energy sector is becoming more and more common. In addition, with the widespread use of SCADA and the increase in automation of camera-based applications, ensuring security in video data communication has become more and more important. In this study, methods that have been successful in providing image security in previous studies in Ref.[1] have been improved to ensure the security of video data communication. These methods use Kurt-Modified Chua’s Circuit (KMCC) as random number generator. Proposed algorithms are efficient for energy sector secure video communication because of using hyperchaotic random number generator and also bit level scrambling, diffusion encryption to each frame of video.