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In this paper, Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) are applied to solve the power loss lessening problem. Protist Algorithm (PA) is modelled based on the Protist's natural activities. Protist exists in moist places. The leading nutritious phase is Plasmodium, the energetic and vibrant phase of Protist. In this segment, the organic substance in Protist search for food in surroundings and conceals enzymes for digestion. Natural actions of Otocyon megalotis are emulated to design the OOA approach. In the projected OOA searching of regions in exploration, for foodstuff the Otocyon megalotis mark the prey in the space is indicated as a global exploration. Real power loss reduction and Voltage stability enhancement are the key objectives of the paper. To solve the problem, Protist algorithm (PA) and Otocyon megalotis optimization algorithm. In the course of the migration procedure, the anterior end outspreads and interconnected arterial system that authorize cytoplasm to stream inside. Then, mutation and cross-over probability are employed to augment the performance of the Protist algorithm (PA). With this integration engendering of the population is done. Mutation classes the population exploration agents (PN) in uphill order conferring to the agents appropriateness (fitness) cost. Consequently, the technique splits the organized agents into three fragments rendering to their fitness value. In which PN/3 denotes to the population possess pre-eminent (aptness) fitness values, subsequently with second pre-eminent and poorest aptness (fitness) values. Then, in this paper, Otocyon megalotis optimization algorithm (OOA) is applied for solving the Power loss lessening problem. In the subsequent segment, navigate during the haunt to seal prey previous to the hit was replicated as a local search. In exploration, the data obtained is shared to all the associates of the family unit for continued existence and growth. Examination of the nearby terrain is modelled with reference to the fitness of all entities. Most excellent entity has investigated the majority fascinating terrain and it will be shared with family unit of Otocyon megalotis. Primarily, Otocyon megalotis show that it not involved in hunting. Conversely, as soon as moving near to prey Otocyon megalotis will perform the attack in quick mode. This approach imitated and designed in the local search segment. Authenticity of the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) is corroborated in 23 benchmark functions, IEEE 30, 57, 300 and 354 test systems. Power Loss reduction achieved with voltage stability enhancement. Real power loss reduction attained. Both the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) performed well in solving the Power loss reduction problem. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity; Sine Cosine Algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness. [ABSTRACT FROM AUTHOR]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper investigates and analyzes the dynamics of the two-dimensional Duffing map. Multistability behavior has been observed from the system numerically. Such behavior, especially the coexistence of chaotic and periodic attractors, is undesirable in the applications of chaos-based cryptography. Therefore, we design and implement a Sine–Cosine chaotification technique to enhance chaos in the multi-stable regions. Furthermore, this paper proposes a new image encryption algorithm to examine the performance of the generalized Duffing map in cryptography applications. Simulation results and security analysis reveal that the proposed algorithm can effectively encrypt and decrypt several image types with a high level of security. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is an Intelligent Transportation System, which in its turn is an application of the Internet of Things. The IoV is a network of connected vehicles that sends/receives messages. However, the moving nature of the vehicles in IoV networks raises a dynamic topology issue. Therefore, establishing effective and dependable communication pathways among vehicular nodes, contingent upon traffic density conditions, presents a growing challenge. As a result, the main contribution of this paper is to propose an efficient network optimization system involving a cluster-based network routing optimizer. The proposed method is a metaheuristic algorithm adapted as a clustering algorithm. For this purpose, the JAYA Algorithm (JAYA) was utilized and integrated with two clustering concepts extracted from K-means Clustering and Automatic Clustering, resulting in proposing JAYA Clustering Algorithm for IoV (JAYACIoV). Subsequently, the performance of the proposed algorithm was investigated utilizing 176 network testing scenarios, and extensive comparative evaluations were conducted against five algorithms from the literature. The results confirmed the superiority of the proposed system with a percentage of 64.82%, where the algorithms were ranked according to the percentage achievement of best results. [ABSTRACT FROM AUTHOR]

This paper proposes a new and improved one-dimensional chaotic system (1DL-SCS). After performance testing, it is verified to have better chaotic performance and a larger range of parameters. Based on this, a new image encryption algorithm is proposed. First, the chaotic sequences generated by the hyper-chaotic Chen system are used to perform twice circular rotations for achieving efficient global scrambling. Then we index scramble the image by row using 1DL-SCS and redistribute the pixel points by column, which further breaks the correlation between image pixels and prepares for diffusion. In the diffusion stage, the plaintext is divided into four parts for processing with the improved generalized Feistel structure, and the plaintext sequence in the round function is further divided into two parts, which greatly improves the diffusion efficiency. Through two rounds of iterative operations, a good diffusion effect can be achieved efficiently. Simulation experiment and performance analysis indicates that the algorithm has outstanding advantages such as excellent ciphertext statistical characteristics, and strong resistance to chosen plaintext attacks. [ABSTRACT FROM AUTHOR]

In recent years, the convergence of the Internet of Things (IoT) and machine learning technologies has instigated advancements in IoT, and machine learning has revolutionized SMEs, reshaping their role in regional economic growth. These technologies provide SMEs with innovative tools, fostering connectivity within regional ecosystems. This transformation is crucial for China's economic realignment in its 'new normal' phase, with enterprises driving social and economic progress. This study proposed the XGBoost algorithm and IoT data enhance collaboration among SMEs in regional economies, fostering innovation and driving economic development. The role of IoT and machine learning in promoting sustainable regional economic prosperity. Technological innovation is essential for enterprise survival and growth in a competitive landscape. This paper examines the influence of environmental factors and geography on economic development across China's regions. It focuses on SME coupling and coordination, contributing to regional economic progress through performance evaluation. IoT integration provides SMEs with abundant real-time data, enabling deep insights into customer behavior, supply chains, and production. Simultaneously, the XGBoost algorithm efficiently processes and extracts actionable insights from this data. Data from 11 provinces in the Yangtze River economic belt highlights Jiangsu Province's leadership in regional innovation performance for 2015 and 2020. The empirical results, grounded in datasets amalgamating information from these provinces, attest to the potential of this IoT and XGBoost-driven approach. Boasting an impressive accuracy rate of 91.7%, surpassing alternative machine learning techniques like SVM, KNN, DT, RF, and LR, this research underscores the effectiveness of this integrated strategy in optimizing SME operations. Additionally, it computes the coupling coordination degree, innovation environment ranking, mean value, and numerical changes from 2010 to 2020, with an average innovation coupling coordination degree of 0.1624 across 30 Chinese provinces. [ABSTRACT FROM AUTHOR]

This paper presents a solution for the security of few-detail images in real-time applications over open or unsecure networks depending on diffusion and confusion operations. The diffusion and confusion operations are both performed based on Henon and Baker chaotic maps. XOR and permutation operations are used to allow diffusion and confusion in the algorithm. In the proposed algorithm, a high-detail image is used as a key. The computer simulation results and security analysis are given to ensure the validity and strength of the proposed algorithm. In the security analysis, we have performed key-space analysis, cropping attack analysis, noise attack analysis, and differential attack analysis. Some statistical analyses based on entropy, histogram, and correlation coefficient estimation are also given to check the strength of the presented algorithm. [ABSTRACT FROM AUTHOR]

It is well recognized that human societies and their problem-solving capabilities have evolved much faster than biological evolution. The inspiration from human behaviors, knowledge exchange, and transformation has given rise to a new evolutionary computation paradigm. Multiple research endeavors have been reported in the literature inspired by the diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in piles of algorithms, worded differently but with more or less similar underlying mechanisms causing immense confusion for a new reader. This paper presents a generalized framework for these socio-inspired evolutionary algorithms (SIEAs) or socio-inspired meta-heuristic algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description and the comparison of each SIEA with the general framework enable a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. This paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research. [ABSTRACT FROM AUTHOR]

The emergence of computer interactive teaching system greatly facilitates the needs of students to learn anytime and anywhere at this stage. At the same time, the increase in online course video, voice, files and other teaching resources also puts forward higher requirements for the operation of the teaching system. This article addresses the practical needs of online teaching and proposes a scalable computer interactive teaching system designed with the aid of large-scale multimedia data analysis algorithms. The system utilizes a principal component analysis linear data feature extraction algorithm to extract data features and implements data prediction through linear transformation methods for data reconstruction. This approach enables the system to effectively extract and analyze data, while also facilitating efficient data prediction for improved teaching outcomes. With its scalability and advanced data analysis capabilities, this system represents a promising solution for enhancing the effectiveness of online teaching and improving the overall learning experience for students. Most online course resources are displayed in the form of multimedia. Therefore, this paper uses robust component analysis to process multimedia data, especially for some problems such as data missing and multimedia data interference. This paper uses RC ranking-related evaluation method and R-value evaluation method to evaluate the prediction data, which proves that parameter selection plays an important role in controlling sparse regression. The algorithm proposed in this paper was tested using actual video and picture data related to the teaching system resources. The results indicated that the algorithm achieved the best data recognition performance when the number of theme models was set to 100. This finding provides an important reference for the system design. Finally, this paper designs a computer interactive teaching system which includes three modules: administrator, student and teacher, and tests the system, which proves the practical operation possibility and application value of the system. [ABSTRACT FROM AUTHOR]

To expand the application scope of the WCA algorithm in the actual process, this research has optimized the problems in the algorithm in two aspects: The first aspect further improves the operating mechanism of the WCA algorithm and improves the performance of optimization problem. In the second aspect, other optimization strategy mechanisms of the WCA algorithm are introduced to enable the algorithm to optimize multi-objective and multidimensional problems. This paper studies a physical test system and sports intelligent based on hybrid wolf pack algorithm and IoT. The system collects and sends data from the data collection terminal of the Web server, receives the data through the wireless module, and sends it to the Web server through the network. The Web server processes and stores the data information to generate a database, and users can view their own sports information by logging in to the Web service program with the account password. In addition, the teacher and administrator accounts have the ability to view all users' exercise information. The system adds three sports items, pull-ups, squats, and standing long jumps. At the same time, the system uses a general motion recognition algorithm, which can effectively reuse and add new sports items. According to the actual needs of intelligent sports training, this paper combines somatosensory technology, bone tracking technology, and motion recognition algorithm to realize a high-precision, low-latency intelligent sports training system. [ABSTRACT FROM AUTHOR]

Due to the large number of stock companies, complex stock categories and inconsistent evaluation standards of market value, it is not conducive to the choice of investors. Based on the theory and practice of traditional stock performance evaluation model, this paper integrates the algorithm thought structure of Fama–French five-factor model and proposes a machine learning algorithm model for stock performance research. In addition, this paper also builds a model that can evaluate the style and timing ability of fund managers to improve the fund performance evaluation system to a greater extent. With the help of the performance evaluation updating function of the model, it provides a new experience material in the empirical research composition of the fund performance field. In the system test module, the former prediction data and the actual experimental data are integrated, and the two are sorted out and compared, which proves the feasibility and effectiveness of the proposed algorithm model. The final experimental results verify the usefulness of the model in stock return prediction and analysis of influencing factors. [ABSTRACT FROM AUTHOR]

Based on the rapid development of big data, cloud computing, Internet of things and other technologies in recent years, intelligent hardware devices has been applied to all aspects of life. Under this background, some scholars have put forward relevant concepts such as "Smart Life". In the field of mass sports life, through the development and application of science and technology, there has been new changes related to the application of neural network algorithm technology and intelligent hardware devices. Therefore, artificial intelligence wearable devices based on wearable technology came into being. This paper analyzes the application of this device in mass sports activities. Then, this paper describes the key research technologies of motion data processing based on neural network algorithm, including: depth frame differential convolution neural network structure, motion data extraction method, human motion signal processing algorithm, etc.; then it analyzes the action recognition and interaction system design based on Intelligent wearable devices. Finally, it analyzes the recognition results of human action system, the accuracy of human action recognition system and the factors that affect the performance of the recognition system. It is concluded that the artificial intelligent wearable devices designed in this paper can be well used in popular sports activities. Finally, it introduces the research on the evaluation strategy of popular sports activities based on artificial intelligence, and hopes that this equipment can help public sports activities. This paper studies the neural network algorithm and applies it to the design process of artificial intelligence wearable devices, which promotes the development of mass sports activity evaluation. [ABSTRACT FROM AUTHOR]

Nowadays, physical dance is widely spread in the society as an emerging sport. Dance movement is favored by people because of its unique social function and fitness effect. For dance teaching, dance movement analysis can help optimize and improve the existing dance movements and the understanding and inheritance of traditional dance movements. With the rise of online teaching, intelligent identification and analysis of dance movements can promote the better development of sports dance teaching. However, the relevant research in this area is still very scarce. As the basis of this kind of research, there is an urgent need for dance motion recognition technology. Based on this background, this paper by introducing neural network algorithm for dance teaching sports image special recognition design, the algorithm can combine feature extraction technology to process video, extract the dance movements in the target data set, then for the extraction of cumulative feature extraction operation, in order to accumulate all the collected target features, so as to further complete the gradient histogram acquisition. Through the design experimental test, the cumulative feature image extraction results obtained through the algorithm are obviously better than the traditional image recognition results, so the design rationality and effectiveness of the algorithm are proved, and the sports dance teaching can be specially assisted. This paper designs an effective auxiliary image recognition algorithm by introducing the neural network algorithm into the field of sports dance teaching. [ABSTRACT FROM AUTHOR]

A profound concern dwelling on developing practical control algorithms for autonomous cars is to guarantee robustness and resilience in harsh driving situations. Extraneous environmental factors coupled with structured and unstructured uncertainties have always posed concerns about the proposed algorithms' effectiveness. This paper presents an improved control algorithm based on a robust adaptive neural network trained with integral sliding mode (NN-ISMC) with the capacity for state estimation and auxiliary control inputs. Additionally, the paper considers the contribution of active front steering (AFS) integrated with direct yaw moment control (DYC) to employ during critical driving scenarios. Moreover, a super-twisting ESO disturbance observer (STESO-DO) was developed to estimate disturbances imposed on the car during an emergency maneuver, such as double-lane change, in terms of successive strong gusts of crosswind. Additional uncertainties were introduced to the system to evaluate the algorithm's robustness, such as the tire cornering stiffness and traveling speed. The performance of the designed framework was further assessed against two documented comparable methods in the literature using high-fidelity MATLAB/Simulink–CarSim co-simulations. The findings from various driving conditions and speeds indicate that the proposed controller successfully stabilizes the handling dynamics and thus enhances the path-tracking performance compared to the previously reported methods. [ABSTRACT FROM AUTHOR]

The purpose of designing embedded Internet of things and studying the ad hoc technology of gateway is to meet the monitoring requirements brought by the continuous expansion of data center. Among them, in the aspect of dynamic data monitoring, this paper designs a dynamic acquisition algorithm, that is, by analyzing the characteristics of data center resources and the number of information acquisition methods, so as to adjust the data acquisition cycle and to reduce the monitoring workload and improve the monitoring flexibility. At the same time, this paper also studies the automatic psychological evaluation system of students. It is found that with the intensification and efficient operation of social competition, some special groups of college students are facing increasing pressure, and events caused by psychological problems emerge one after another. Even though there are many psychological evaluation systems on the network, a large number of psychological knowledge and mental health management on the Internet cannot effectively manage and improve the psychological status of college students. Therefore, the university needs to build a psychological evaluation system for college students in time and combine the automatic psychological evaluation system for college students with embedded Internet of things and cloud service technology to improve the accuracy of psychological evaluation. The application of the system, college students can independently evaluate their own mental state, if there are psychological problems can be timely counseling, not only can improve the level of mental health education in colleges and universities, but also can promote the development of physical and mental health. [ABSTRACT FROM AUTHOR]

Data mining algorithms can process target data and extract useful hidden information, which is helpful for decision making. However, current mining algorithms have some shortcomings such as time-consuming processing of big data or inability to process massive data. Since data mining technology cannot be used in the traditional cloud platform environment, it is necessary to improve the algorithm to make it more suitable for the cloud platform environment. By analyzing the actual application process of BP classification algorithm, this paper expounds the practicability of BP classification algorithm, analyzes the data mining process based on Hadoop cloud platform, and expounds the development ideas of BP classification algorithm. The source of data mining algorithm supported by cloud computing is discussed. Finally, Hadoop cloud platform. This paper designed the corresponding system architecture and data interface, and established a suitable test environment for this system, and completed the simulation experiment test through the design. The calculation time of this algorithm is proportional to the amount of data, showing a linear relationship. In data mining, the optimized BP algorithm in this paper can significantly save resources in terms of spatial features. This paper designs an optimized operating system based on Hadoop platform through comprehensive analysis and improvement algorithm. [ABSTRACT FROM AUTHOR]

With the continuous progress of Internet technology, so there is a higher demand for English translation. In this context, this paper realizes the design and improvement of the online English translation system by combining the image segmentation algorithm. After the experimental test of the algorithm of this system, if there is a lot of salt-and-pepper noise in the target image in the actual system application process, the method designed in this paper can reflect a better processing effect. Based on the analysis of performance parameters, this method can effectively process Gaussian noise images with high precision. After the completion of the system design, this paper tests the system. From the test and operation results, the software has reached the design objectives and completed the work tasks. The English translation online teaching system can form a safe, stable and high-bandwidth English translation teaching network and can realize the connection of the whole network. Users can share various software and hardware resources, completed the design of a translation platform that can realize rapid English communication, share experience and discuss, to effectively improve the learner's initiative. In this paper, an effective online teaching system is designed by introducing image segmentation algorithm into the field of English translation. [ABSTRACT FROM AUTHOR]

When constructing the algorithm model of sports training action classification, the accuracy of action classification has an important impact on the algorithm model. How to improve the algorithm model to improve the accuracy of sports training action classification needs further research. Based on BP neural network algorithm, this paper carries out the modeling of BP neural network signal classification algorithm and the construction of BP neural network, and deduces the BP algorithm in detail. Firstly, this paper applies genetic algorithm to the initial parameter selection of BP algorithm to avoid the local optimization problem. When carrying out chromosome coding, binary coding is easy to cause the problem of too long coding string, which also needs to be restored and decoded. The algorithm runs too long and the learning accuracy is not high. Therefore, this paper uses real coding. Through simulation analysis, it can be seen that the classification accuracy of the improved algorithm model is significantly higher than that of the simple BP algorithm. In addition, this paper analyzes the requirements of the sports training management visualization system, introduces the system structure framework and network topology, describes in detail the functions of the user information management module, the training plan management module, the training test management module, the competition information management module and the scientific research information management module, and tests the visualization function of the system. Finally, this paper analyzes the problems existing in the current sports training management, and puts forward the development strategy of sports training management based on this, which lays a theoretical foundation for the scientific development of sports training. [ABSTRACT FROM AUTHOR]

Today, data storage technology is also gradually improving. Various industries can store massive amounts of data for analysis. The global climate change and the bad ecology led to frequent occurrence of natural disasters. Therefore, it is necessary to establish an effective emergency materials distribution system. The neural network model is used to calculate and the optimal emergency distribution route is analyzed according to the historical information and the data. Considering backpropagation, this paper further disposing a method to further improve the calculation of neural network algorithm. From the perspective of structural parameters of neural network algorithms, this paper uses genetic algorithms to construct predictions, and combines the actual purpose of material distribution after disasters. Considering the capacity constraints of distribution centers, time constraints, material needs of disaster relief points and different means of transportation, a dual-objective path planning with multiple distribution centers and multiple disaster relief points with the shortest overall delivery time and lowest overall delivery cost is constructed. By establishing an emergency material distribution system, it can maximize the prompt and accurate delivery after a natural disaster occurs, and solves the urgent needs of the people. [ABSTRACT FROM AUTHOR]

Spectral clustering has become a prevalent option for data clustering as it performs well on non-convex spatial datasets with sophisticated structures. The spectral clustering effects depend on the construction of the similarity graph matrix. In this paper, in order to further enhance the clustering performance, we propose a novel similarity measure function based on neighbor relations. The proposed method is called SC-NR. It uses the Gaussian kernel function to measure the similarity between two objects. Since Euclidean distance cannot fully reflect the relation between data, this method adds a weight related to the order of nearest neighbors to the distance between two points. The similarity is better expressed by weighted-Euclidean distance. In experiments, we compared the proposed method with the previous works via the external indexes, that is, clustering accuracy (ACC), normalized mutual information (NMI), and F-measure. The comparison of indexes with state-of-the-art methods demonstrates the superiority of our algorithm. The experiment includes six synthetic datasets and twelve real-world datasets. For instance, in the PenDigits dataset F-measure metric is 16.50% higher than the current algorithms. [ABSTRACT FROM AUTHOR]

Infrared small and dim target has posed great challenges on the target detection and tracking, especially for early warning, navigation and guidance. However, most common tracking algorithms are inefficient for infrared small target due to huge computation, and complicated feature analysis on such target is useless and needless. As such, a fast and useful algorithm based on the kernel correlation filter (KCF) is proposed in this paper. It supplies a solution for target without clear characteristic, target missing and analog interference in complex backgrounds. The proposed algorithm focuses on model update and scale update rules, fusion feature expression and multi-cues constrained re-detection module; all of the ideas aim to optimize process and improve practicability. Firstly, the improved ridge regression model with solving boundary effect and model consistency can get more precise results. Secondly, a useful strengthened feature description (SFD) including the fused feature extraction and complete confidence indicator is proposed to improve the reliability of tracking. Thirdly, the stabilized re-detection (SR) strategy is designed based on motion rules, which can cope with abnormal disturbance and missing states. Fourthly, an adaptive model update is proposed, which can reduce the error accumulation. The experiment results and the comparative studies with some common algorithms show that the proposed algorithm achieves the success rate of tracking above 98%, RMSE of center location below 5 pixels and speed above 120 fps and tends to be more effective. [ABSTRACT FROM AUTHOR]

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller. [ABSTRACT FROM AUTHOR]

Economic progress has been founded on environmental pressure by generating all types of environmental harm, such as an increase in greenhouse gases in the atmosphere and significant climate change, which has forced people to reflect. Building a people-oriented, comprehensive, coordinated system to compile experience and lessons assists the government in formulating appropriate policies according to the meaning of the scientific development viewpoint. Based on the above, this research designs Cost–Benefit Decision Tree (CBDT) Algorithm for the economic protection development level using a Decision Tree (DT) and Ensemble Algorithm. In the design of ML Algorithm, this research work initially introduces the Decision Tree model for economic protection evaluation by emphasizing its value as a basic machine-learning framework for fully examining many aspects of economic protection development. Second, it investigates the critical role of Ensemble Algorithms, such as Random Forest in augmenting the capabilities of DT. Third, the paper focuses on the development of a complete Economic Protection Evaluation Index System using the DT Algorithm. To provide a robust, scientifically sound, and practicable framework for evaluating economic protection, this approach integrates scientificity, comparability, systematization, hierarchy, relative decoupling, and data availability principles. Finally, the study constructs the suggested ML Algorithm using the insights from the preceding parts. This algorithm combines the Decision Tree and Random Forest models to generate a single, effective tool for evaluating economic development levels. The experimental findings suggest that the proposed hybrid assessment technique is effective across several dimensions. The proposed CBDT demonstrated its capacity to adapt to changing data circumstances in evaluating low-carbon efficiency using real-world data from 30 major Chinese cities. Furthermore, the algorithm regularly beats rival techniques regarding efficiency improvements, with an average improvement of 12.5% across all cities. Notably, it outperforms other algorithms regarding computational efficiency, with a quicker execution time and an exceptional accuracy rate of 93.2%. The proposed ML Algorithm improves decision-making in the economic protection development level field by effectively balancing cost and benefit considerations. It will improve accuracy and efficiency to outperform existing approaches, thereby advancing economic protection evaluation and decision-making processes in this domain. [ABSTRACT FROM AUTHOR]

The environmental perception system is the foundation of unmanned driving systems and also the fundamental guarantee of the safety and intelligence of unmanned vehicles. The obstacle hazard identification technology is the core of the environment perception system, and it is also the basic condition for the autonomous driving of unmanned vehicles. In view of the complexity of obstacle danger identification, this research paper designs an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm for hazard recognition of obstacles in unmanned scenes through a systematic approach. First, it highlights the significance of morphological component analysis in identifying non-smooth regions within images where obstacles are likely to be present. Second, it introduces a novel approach for core point definition by identifying an optimal MinDensity value based on the curvature of the density distribution curve. Third, it addresses variations in density sequences through smoothing and normalization. Finally, it constructs an improved DBSCAN Algorithm for hazard recognition of obstacles in unmanned scenes. It addresses limitations in the traditional DBSCAN by refining the core point definition using an adaptive density threshold. It identifies the "elbow point" in density distribution, enhancing its ability to distinguish density states. Additionally, it incorporates density curve smoothing, normalization, and a merger step for handling stationary objects. The results show that it has high accuracy (95.6%), precision (96.3%), recall (94.5%), and F-Score (95.4%), as well as increased consistency (92.5%) and dependability (93.2%). It also has fast real-time data processing, lasting only 0.12 s, making it an excellent choice for obstacle detection and unmanned hazard avoidance. [ABSTRACT FROM AUTHOR]

In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting objectives and hundreds even thousands of decision variables are contained. Therefore, it is a great challenge to address LSMOPs due to the curse of dimensionality. To tackle LSMOPs, this paper proposes a resource allocation-based multi-objective optimization evolutionary algorithm. In the proposed algorithm, decision variables are firstly divided into convergence-related variables and diversity-related variables by the proposed layer thickness-based variable classification (LTVC) method. Then, a resource allocation-based convergence optimization strategy is introduced for the convergence-related variables, which can allocate more computational resource to the sub-component with the best contribution. For the diversity-related variables, diversity optimization technique is adopted. Finally, the experimental results verify that the proposed algorithm has a competitive performance compared with some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithms tend to be limited to local optimization when solving complex optimization problems. Different iteration schemes lead to different convergence speeds. In this paper, we mainly use the dynamic mutation parameter FS to improve the DE algorithm. Based on two ideas, a total of seven DE schemes are proposed to optimize the DE algorithm. We test the performance of the improved DE scheme on 56 test functions. Experiments show that the improved DE algorithm is better than the baseline DE algorithm in terms of accuracy, convergence and8 convergence speed. [ABSTRACT FROM AUTHOR]

This paper aims to assess and deal with the challenges experienced by medical professionals caring for infectious diseases. In Pakistan, public health is still a serious concern and the main contributor to morbidity and mortality is infectious diseases. The major issue is a resemblance in the clinical symptoms of infectious diseases such as tuberculosis, hepatitis, COVID-19, dengue, and malaria. Early detection of infectious disease is crucial in order to start treatment with counseling and medication. This can only be done if several infections with similar clinical traits can be diagnosed depending on several criteria, including the availability of various kits, the ability to carry out diagnostic procedures, money, and technical staff. But woefully Pakistan's economy is badly battered due to several circumstances. Therefore, we are unable to provide patients with enough diagnostic testing kits and broadly accessible therapy choices, which makes diagnosis more difficult and create hesitancy with fuzziness and randomness. For this purpose, we introduced the new concept of the complex probabilistic hesitant fuzzy N-soft set. We defined its fundamental operations (like restricted and extended union, restricted and extended intersection, weak, top and bottom weak complements, as well as soft max-AND or soft min-OR) with examples. We also discussed their many properties with their proofs and theorems. Furthermore, we developed the algorithms for decision-making where doctors use the complex probabilistic hesitant fuzzy N-soft information to identify a particular disease. Furthermore, we explained numerical illustration of two case studies. Moreover, a sensitive and comparative analysis is discussed. In the last, we conclude the whole study. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) has been applied to solve various optimization problems due to its simplicity and high search efficiency. However, researchers have confirmed that it still has some shortcomings such as premature convergence and slow convergence, especially when dealing with complex optimization problems. To address these concerning issues, this paper proposes a hybrid particle swarm-differential evolution algorithm (HPSDE). Firstly, to enhance the optimization performance, a modified updating scheme named particle-swarm mutation strategy is designed and an improved control parameters adaption is developed. Then, DE/rand-to-rand/1 mutation strategy is adopted to increase the population diversity and enhance the ability of particles escaping away from local optima. To achieve an improved DE variant with rapid convergence and fine stability, a random mutation framework is designed to combine the two mutation strategies mentioned above. To evaluate the efficiency of HPSDE algorithm, four different experiments have been taken on twenty-nine benchmark functions. The numerical results validate that HPSDE has better overall performance than the other competitors. Additionally, HPSDE is successfully applied to solve five typical engineering optimization problems. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-objective variant of the marine predators algorithm (MPA) called the multi-objective improved marine predators algorithm (MOIMPA), which incorporates concepts from Quantum theory. By leveraging Quantum theory, the MOIMPA aims to enhance the MPA's ability to balance between exploration and exploitation and find optimal solutions. The algorithm utilizes a concept inspired by the Schrödinger wave function to determine the position of particles in the search space. This modification improves both exploration and exploitation, resulting in enhanced performance. Additionally, the proposed MOIMPA incorporates the Pareto dominance mechanism. It stores non-dominated Pareto optimal solutions in a repository and employs a roulette wheel strategy to select solutions from the repository, considering their coverage. To evaluate the effectiveness and efficiency of MOIMPA, tests are conducted on various benchmark functions, including ZDT and DTLZ, as well as using the evolutionary computation 2009 (CEC'09) test suite. The algorithm is also evaluated on engineering design problems. A comparison is made between the proposed multi-objective approach and other well-known evolutionary optimization methods, such as MOMPA, multi-objective ant lion optimizer, and multi-objective multi-verse optimization. The statistical results demonstrate the robustness of the MOIMPA approach, as measured by metrics like inverted generational distance, generalized distance, spacing, and delta. Furthermore, qualitative experimental results confirm that MOIMPA provides highly accurate approximations of the true Pareto fronts. [ABSTRACT FROM AUTHOR]

Meta-heuristics have been successfully applied to many complex optimization problems. One of the main reasons for its success is its ability to handle non-convex, nonlinear, multimodal, multi-variable, and multi-objective problems with easy implementation. However, the quality of the response of these algorithms to an optimization problem is highly susceptible to the control parameters, and few works aim to tune them or find tools that can improve the algorithms. The literature is rich in proposals for new algorithms, but not for improving existing ones. This paper presents different strategies for tuning and accelerating meta-heuristics using the first hybrid algorithm in the literature. The Lichtenberg algorithm is inspired by lightning and Lichtenberg figures and has been increasingly successfully applied to various optimization problems. However, a study of its best parameters has never been presented until now. After a discussion of the best tuning tools, its tuning parameters are performed using response surface methodology. Then, 14 versions are studied through 10 test functions using chaos theory and Lévy flights scenarios. After 13,500 simulations, the chaotic Lichtenberg algorithm equipped with the piecewise function and tuned parameters proved the best version with only 16% similarity to the original algorithm. Then, it was compared to the genetic algorithm, particle swarm optimization, gray wolf optimizer, salp swarm optimization, whale optimization algorithm, and dragonfly algorithm. The proposed algorithm had both the best average accuracy, lower computational cost, and the smallest standard deviation. [ABSTRACT FROM AUTHOR]

In recent years, the cuckoo search (CS) algorithm has been successfully applied to single-objective optimization problems. But in real life, most optimization problems are multi-objective optimization problems (MOPs). In order to enable CS to better solve MOPs, this paper proposes an elite-guided multi-objective cuckoo search algorithm based on cross-operation and information enhancement (CIE-MOCS). This algorithm first enhances its population diversity through crossover operation, then adds elite individuals to guide its update process to speed up the algorithm convergence speed. Finally, the method of information enhancement is adopted in the abandonment process, so that the algorithm is not easy to fall into the local optimum. In order to verify the performance of the algorithm, this paper uses a variety of benchmark functions and performance evaluation indicators to evaluate it, and provides a case to verify the effectiveness of the algorithm in practical applications. The experimental results show that CIE-MOCS has good performance compared with the contrasting algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes PatternAttack to mitigate two major issues of Adversarial Transformation Network (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on patterns to generalize ATN. This proposed autoencoder could support different patterns such as all-pixel pattern, object boundary pattern, and class model map pattern. In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of L 0 -norm and L 2 -norm. This algorithm employs adversarial pixel ranking heuristics such as JSMA and COI to prioritize adversarial pixels. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder generates diverse adversarial examples. For the second issue, the proposed algorithm significantly improves the quality of adversarial examples. In terms of L 0 -norm, the proposed algorithm decreases from hundreds of adversarial pixels to one adversarial pixel. In terms of L 2 -norm, the proposed algorithm reduces the average distance considerably. These results show that the proposed method can generate high-quality and diverse adversarial examples in practice. [ABSTRACT FROM AUTHOR]

The emerging technologies such as high-performance computing, cloud computing and large data centre networks demand very high injection rate, high scalability and very low switching latency (SL) that can manage high network traffic, and buffering and also be cost-effective, so that they can be a viable solution for future manufacturing industry. To overcome the biggest challenge of optical buffering and big quantity of data switching, the Data Vortex (DV) all optical switch innovations and its various enhancements such as Augmented Data Vortex (ADV) seem to be the possible solution and can bring the revolution in the industrial world. In this paper, a new generalized algorithm has been used to simulate the equivalent planar virtual model of 3-D ADV and is named as equivalent planar algorithm (EPA). The algorithm is more versatile and provides more flexible design for multiple level networks. To verify the proposed EPA of ADV (or EPA ADV), the simulations have been performed for various input traffic loads and switch size keeping active angle 'A' constant. Also the ADV switch is not studied under asymmetric mode (AM) of operation till now which has been done here. Lastly the paper also provides the performance comparison study of original DV and EPA ADV under symmetric and asymmetric I/O mode. The results obtained from simulation have shown that under modest degree of asymmetric I/O operation of the switch EPA ADV shows performance improvement in injection rate (throughput) and average switch latency (SL) under different traffic pattern as compared to symmetric mode (SM) of operation in ADV. [ABSTRACT FROM AUTHOR]

The main purpose of identifying and locating the retina vessels is to specify from the fundus image the various tissues of the vascular structure, which can be wide or tight. The classification of vessels in the retinal image often confronts several challenges, such as the low contrast accompanying the fundus image, the inhomogeneity of the background lighting, and the noise. Moreover, fuzzy c-means (FCM) is one of the most frequently used algorithms for medical image segmentation due to its effectiveness. Hence, many FCM method derivatives have been developed to improve their noise robustness and time-consuming. This paper aims to analyze the performance of some improved FCM algorithms to recommend the best ones for the segmentation of retinal blood vessels. Eight derivatives of FCM algorithm are detained in this study: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and SSFCA. The performance analysis is conducted from three viewpoints: noise robustness, blood vessels segmentation performance, and time-consuming. At first, the noise robustness of improved FCM clustering algorithms is evaluated using a synthetic image degraded by various types and levels of noise. Then, the ability of the selected algorithms to segment retinal blood vessels is assessed based on images from the DRIVE and STARE databases after a pre-processing phase. Finally, the time consumption of each algorithm is measured. The experiments demonstrate that the FRFCM and DSFCM_N algorithms achieve better results in terms of noise robustness and blood vessels segmentation. Regarding the running time, the FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images. The results of this study are extensively discussed, and some suggestions are proposed at the end of this paper. [ABSTRACT FROM AUTHOR]

Path planning is an NP-hard problem in road network environments. Considering that the existing path planning algorithms mainly have the problems of low smoothness and low search efficiency in generating paths in large-scale complex environments, an improved rapidly exploring random tree (RRT) algorithm is proposed in this paper. First, the grid method is applied to model the road network environment, and the RRT algorithm based on adjacency expansion is proposed to search the initial path. Then, the strategies of identifying paths and eliminating redundant paths are adopted, respectively, to further optimize the selected paths. Experimental results show that, compared with other path planning algorithms, our algorithm can achieve faster convergence speed, shorter search path, and better smoothness in a complex map of the environment. [ABSTRACT FROM AUTHOR]

Real-parameter single objective optimization has been studied for decades. In recent, a new setting is applied in this field based on the consideration that solving difficulty scales exponentially with the increase in dimensionality. Under the new setting, differential evolution (DE) still outstands in performance as before. Meanwhile, a new type of population-based metaheuristic—gaining–sharing knowledge-based algorithm, becomes a dark horse. Furthermore, ensemble of the above two types of algorithm is proposed in the literature. Although such ensemble shows good performance, provided that a more reasonable scheme is used for the communication between the constituent algorithms, better ensemble can be obtained. We believe that the new scheme should be with adaptiveness. In this paper, we propose an adaptive scheme for the communication. According to the scheme, individuals chosen based on fitness and lifetime are exchanged. In fact, in the field of DE, it is rare to consider lifetime of individual. However, lifetime is no less important than fitness in our scheme. In our experiment, our ensemble is compared with seven state-of-the-art algorithms. According to experimental results, our ensemble is comparable to one of the peers and better than the other ones. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of existing algorithms in solving mixed-integer programming problems, such as local convergence and poor solution accuracy, this paper presents a hybrid improved sine and cosine algorithm. The improved position update formula is introduced to enhance the global and local search abilities of the algorithm, and combined mutation is further given to avoid local convergence. The performance of proposed algorithm is validated by the high-dimensional modified CEC 2017 COPs and two complex engineering optimization problems. The experimental findings indicate that the hybrid mechanism of improved sine and cosine algorithm and combined mutation is demonstrably effective, and the proposed algorithm provides a satisfactory solution for addressing high-dimensional complicated mixed-integer programming problems. [ABSTRACT FROM AUTHOR]

Although a wide variety of background subtraction methods has been proposed in recent years, none has been able to fully address multi-scale moving objects and dynamic background in real surveillance tasks. In this paper, a novel and effective background subtraction method, named regional multi-feature-frequency (RMFF), is proposed to detect multi-scale moving objects under dynamic background. Unlike many existing methods construct background model using simple multi-feature combinations, RMFF exploits the spatiotemporal cues of multi-feature as well as superpixels at each scale, thus allowing for more robust information to be exploited for background modeling. Specifically, the spatial relationship between pixels in a neighborhood and the frequencies of features over time are first exploited, enabling accurate detection of moving objects while ignoring most dynamic background changes. Then, the use of multi-scale superpixels for exploiting the structural information existing in real-world scenes further enhances robustness to multi-scale objects and environmental variations. Finally, an adaptive strategy is employed to dynamically adjust the foreground/background segmentation threshold for each region without user intervention. This adaptive threshold is defined for each region separately, and can adjust dynamically based on continuous monitoring of the background changes, thereby effectively reducing potential segmentation noise. Experiments on the 2014 version of the ChangeDetection.net dataset demonstrate that the proposed method outperforms the 12 state-of-the-art algorithms in terms of overall F-Measure and performs effectively in many complex scenes. Consequently, it is verified that the developed approach is feasible and useful for robust application in practical video surveillance. [ABSTRACT FROM AUTHOR]

Community discovery is a vital link in the research of social networks aiming at the shortcomings of the current local extension-based community discovery algorithm in local community discovery and extension. In this paper, we proposed a algorithm based on relationship similarity and local extension overlapping community detection (RSLO). First, use the node's relationship similarity strategy to find close seed communities. Then, according to the discovered seed community, the similarity between the neighboring nodes of the community and the community is calculated, and the nodes whose similarity meets the threshold are selected. After that, an adaptive optimization function is used to expand the community. Finally, the free nodes that have not been divided into the community are divided into communities, thereby achieving a more comprehensive community discovery. We conduct experiments on classic datasets and artificially generated networks. The results show that the RSLO algorithm can find accurate and objective community structures. [ABSTRACT FROM AUTHOR]

In this paper, we study numerical solution of ruin probability of continuous time model. We develop an effective optimal adaptive particle swarm optimization-triangular neural network (PSO-TNN), which consists of three parts: particle swarm optimization algorithm (PSO) improved trigonometric function, extreme learning machine algorithm with initial conditions (IELM) and improved reduction algorithm. The results obtained that PSO-TNN is superior to triangular neural network (TNN) and physics-informed neural networks (PINNs), and PSO is superior to Aquila Optimizer (AO), Smell Agent Optimization (SAO), African vultures optimization algorithm (AVOA), Arithmetic optimization algorithm (AOA) in the optimization of neural network. Because the relationship between the number of neural networks and the mean square error is uncertain, we propose the adaptive reduction algorithm (AR). Through the comparison of numerical solutions with the analytical solutions and traditional numerical solutions, the PSO-TNN algorithm clearly reduced the mean square error and relative error. The PSO-TNN algorithm shows a clear improvement in terms of accuracy and overall efficiency. [ABSTRACT FROM AUTHOR]

Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several approaches have been proposed in the literature based on spatial and fr uency domain techniques. Among them, the Incomplete Beta Function (IBF) is a popular scheme used for image contrast enhancement. In the IBF based image contrast enhancement technique, quality of an image is improved by two controlling parameters. Under such conditions, these parameters need to be tuned for obtaining better outcomes. In this paper, a new gray-scale contrast enhancement algorithm is introduced where, instead of tuning the controlling parameters of IBF experimentally, their near-optimal values are calculated with the help of a recently published meta-heuristic algorithm called Artificial Electric Field Algorithm (AEFA). The proposed method has been compared with many state-of-the-art techniques in terms of some standard metrics considering three different datasets, namely Kodak, MIT-Adobe FiveK and USC-SIPI. Simulation results demonstrate that the proposed AEFA based image enhancement technique increases the overall image contrast and enriches the information present in the image. [ABSTRACT FROM AUTHOR]

In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity outperforms that of its classical counterpart. On the other hand, we propose a Deep self-learning approach for modeling the improvement of two Swarm Intelligence Algorithms, namely Artificial Orca Algorithm (AOA) and Elephant Herding Optimization (EHO) in order to improve their effectiveness. The deep self-learning approach is based on two well-known dynamic mutation operators, namely Cauchy mutation operator and Gaussian mutation operator. And in order to improve the efficiency of these algorithms, they are hybridized with QOPTICS and executed on just one cluster it yields. This way, both effectiveness and efficiency are handled. To evaluate the proposed approaches, an intelligent application is developed to manage the dispatching of emergency vehicles in a large geographic region and in the context of Covid-19 crisis in order to avoid an important loss in human lives. A theoretical model is designed to describe the issue mathematically. Extensive experiments are then performed to validate the mathematical model and evaluate the performance of the proposed deep self-learning algorithms. Comparison with a state-of-the-art technique shows a significant positive impact of hybridizing Quantum Machine Learning (QML) with Deep Self Learning (DSL) on solving the Covid-19 EMS transportation. [ABSTRACT FROM AUTHOR]

This paper presents a Q-learning-based algorithm for sequence and orientation optimization toward the 2D rectangular strip packing problem. The width-filled skyline is used to represent the interior packing state, and a constructive rectangular packing algorithm with the commonly adopted fitness evaluation for placement is designed. Then, the consecutive item packing is simulated as Markov Decision Process, where the state is defined as the set of already packed items, and the action is defined as the rectangle selected to be packed along with its orientation. We propose the reverse updating of Q-value in the paradigm of Q-learning and use the algorithm to optimize the sequence and orientation of the rectangles. The decreasing-size-choice mechanism in Q-learning is studied on randomly generated problems to optimize the setting of ε-greedy policy. We also test the Q-learning-based algorithm on the benchmark instances of C21, N13, N-series from NT, Cgcut and Beng. Compared with a few state-of-the-art algorithms, the computational results show that the proposed algorithm can produce good packing quality when adequate execution time allowed. [ABSTRACT FROM AUTHOR]

In this paper, septic B-spline approach with linearization and without linearization technique with the help of Butcher's fifth-order Runge–Kutta scheme is implemented to obtain solitary wave solutions of the modified regularized long wave equation. The error norms L 2 and L ∞ and the three invariants I 1 , I 2 and I 3 are computed on three tests to confirm about the efficiency and accuracy of the proposed methods. The stability analysis is performed using Von-Neumann technique on the linearized septic B-spline approach. The results of the present work are compared with the analytical and existing solutions. [ABSTRACT FROM AUTHOR]

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly. [ABSTRACT FROM AUTHOR]

Transportation has always been an indispensable part of people's lives. With the growth of the total population, the number of trips continues to increase, traffic pressure continues to increase, and road traffic accidents are increasing every year. With the continuous development of artificial intelligence technology, the emergence of intelligent transportation systems has greatly promoted the development and commercialization of irreversible intelligent transportation systems. This paper aims to study the road vehicle recognition algorithm in safety-assisted driving based on artificial intelligence. The research topic of this paper is the research of road vehicle recognition algorithm in safety-assisted driving. The research focus is on lane detection algorithm and vehicle detection algorithm. The concept of lane line detection is proposed, and the image preprocessing method is introduced. The experimental results in this paper show that when the road environment is complex, the vehicle itself will cause misjudgments, but the overall correct rate is over 92.5%. [ABSTRACT FROM AUTHOR]

With the gradual advancement of image sensor technology and the gradual complexity of the application environment, single-source imaging sensors have been difficult to meet practical application requirements. Therefore, the registration and fusion techniques of infrared images and visible images are hot topics in recent years. This article aims to study the fusion of two images. Based on the traditional image fusion algorithm, the color space IHS transform and the lifting wavelet transform are combined according to the characteristics of infrared image and visible light image captured by the UAV imaging device. The experimental results show that the algorithm can not only retain the brightness information of the infrared target. Among them, some details of the visible light image are retained. In this paper, the four objective criteria of information entropy, standard deviation, image mean and average gradient are used to evaluate the fusion effect of the proposed algorithm. The obtained values are 7.285, 29.487, 122.2739, and 4.5678, respectively, which are higher than other algorithms. It can be seen that the algorithm proposed in this paper has important practical significance. [ABSTRACT FROM AUTHOR]

Two goals of multi-objective evolutionary algorithms are effectively improving their convergence and diversity and making the Pareto set evenly distributed and close to the real Pareto front. At present, the challenges to be solved by the multi-objective evolutionary algorithm are to improve the convergence and diversity of the algorithm, and how to better solve functions with complex PF and/or PS shapes. Therefore, this paper proposes a gray wolf optimization-based self-organizing fuzzy multi-objective evolutionary algorithm. Gray wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on 15 problems of GLT1-6 and WFG1-9 and the algorithm proposed in this paper achieve the best on 18 values. And the result shows that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]