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Abstract: The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm. [Copyright &y& Elsevier]

The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships' single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper's algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution's efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines. [ABSTRACT FROM AUTHOR]

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In this paper, a 30 dB broadband microstrip directional coupler is designed by using a discrete coding type coupling structure with a co‐simulation technique. Combined with the proposed hybrid binary particle swarm genetic optimization algorithm (HBPSGOA) and the carefully designed fitness function, the discrete coding type structure can provide satisfactory flexibility and excellent performance. For a fixed design space, the discrete space can firstly be encoded and mapped to the corresponding coding matrix, and then the optimal coding matrix searched by HBPSGOA will be used to design a broadband coupler with high directivity. The effectiveness of the proposed HBPSGOA, binary particle swarm algorithm (BPSO), and genetic algorithm (GA) to design a discrete coding type coupler is tested. Experimental results prove that the HBPSGOA proposed in this paper has higher optimization efficiency. For the 30 dB broadband microstrip directional coupler designed in this paper, the center frequency is at 2 GHz and the fractional bandwidth is 39.4%. The minimum directivity of the designed coupler is 20 dB in the operating band and the maximum directivity is 39.3 dB at 1.9 GHz. In addition, the reason why the coupler can work in a wideband is investigated. [ABSTRACT FROM AUTHOR]

Drones, specifically quadcopters, have increased in importance during the last years due to their wide range of applications, from civil applications to military employment. One of the most important issues in quadcopters is the efficient control system. While many researchers have dealt with building control systems for symmetric quadcopters, this work presents an efficient control system for asymmetric quadcopters using evolutionary computations. The problem is well-defined throughout the paper, and the methodology is explained in detail in the respective sections. A genetic algorithm is used to tune the weighting matrix of the control system after formulating the control system as an optimization problem. The genetic algorithm was fast and active to increase the performance of the proposed system. [ABSTRACT FROM AUTHOR]

Studying the impact of green and high-quality development is of great significance to the healthy growth and sustainable development of enterprises. This paper discusses the influencing factors of the green and high-quality development of enterprises from the perspective of ownership structure and innovation ability, aiming to clarify the impact mechanism of these influencing factors on the green development of enterprises, and combined with emerging machine learning technologies, to propose a novel and effective corporate green high-quality development using a regression prediction model for quality development. Linear regression and one-way ANOVA were used to analyze the influence of each variable on the green and high-quality development of the enterprise, and the weight proportions of each influencing factor under the linear model were obtained. Two machine learning models based on the random forest (RF) algorithm and support vector machine algorithm were established, and the random parameters in the two machine learning algorithms were optimized by a genetic algorithm (GA). The reliability and accuracy of machine learning models and multivariate linear models were compared. The results show that the GA–RF model has superior regression performance compared with other prediction models. This paper provides a convenient machine learning model, which can quickly and effectively predict the green and high-quality development of enterprises, and provide help for enterprise decision-making and government policy formulation. [ABSTRACT FROM AUTHOR]

Distribution process is the core of logistics enterprise system. Efficient distribution process is the key for enterprises to improve logistics service level, gain competitive advantage, and win customers. In order to reduce the design error of the system and ensure the effective operation of the system, this paper establishes the system model and analyzes the properties of the model through the evaluation and analysis of various resources of the distribution system. The future development of intelligent logistics system is not only the key for logistics enterprises to win competition, but also a new measure to promote China's economic development. This is not only a typical NP hard problem, but also a major challenge for the rational and scientific development of the intelligent logistics industry. Based on the previous theoretical research results, this paper intends to explore the intelligent logistics distribution route selection scheme by using mathematical modeling methods such as particle swarm optimization, so as to provide new technologies and methods for logistics distribution operation and management decision-making. The results show that compared with the traditional genetic algorithm, the accuracy of the improved particle swarm optimization algorithm is improved by 10.36%. This method effectively improves the operation cycle and link efficiency and achieves the purpose of optimization. The improved particle swarm optimization algorithm proposed in this paper is not only more suitable and effective for enterprise decision makers to deal with subjective judgments in an imprecise environment. It is also based on the evaluation sequence of each indicator of the alternative obtained from the evaluation and the comprehensive evaluation of all indicators. By weighting and considering the results, the best solution for enterprise logistics and distribution is selected. [ABSTRACT FROM AUTHOR]

This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For instance, on the TIMIT babble noise dataset (120 speakers), feature fusion achieves a remarkable speaker identification accuracy of 92.7%, while various feature optimization techniques combined with K nearest neighbor (KNN) and linear discriminant (LD) classifiers result in a speaker verification equal error rate (SV EER) of 0.7%. Notably, this study achieves a speaker identification accuracy of 93.5% and SV EER of 0.13% on the TIMIT babble noise dataset (630 speakers) using a KNN classifier with feature optimization. On the TIMIT white noise dataset (120 and 630 speakers), speaker identification accuracies of 93.3% and 83.5%, along with SV EER values of 0.58% and 0.13%, respectively, were attained utilizing PCA dimension reduction and feature optimization techniques (PCA-MPA) with KNN classifiers. Furthermore, on the voxceleb1 dataset, PCA-MPA feature optimization with KNN classifiers achieves a speaker identification accuracy of 95.2% and an SV EER of 1.8%. These findings underscore the significant enhancement in computational speed and speaker recognition performance facilitated by feature optimization strategies. [ABSTRACT FROM AUTHOR]

To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system. [ABSTRACT FROM AUTHOR]

Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition. [ABSTRACT FROM AUTHOR]

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation. [ABSTRACT FROM AUTHOR]

When the pulse current method is used for partial discharge (PD) monitoring of mining cables, the detected PD signals are seriously disturbed by the field noise, which are easily submerged in the noise and cannot be extracted. In order to realize the effective separation of the PD signal and the interference signal of the mining cable and improve the signal-to-noise ratio of the PD signal, a denoising method for the PD signal of the mining cable based on genetic algorithm optimization of variational mode decomposition (VMD) and wavelet threshold is proposed in this paper. Firstly, the genetic algorithm is used to optimize the VMD, and the optimal value of the number of modal components K and the quadratic penalty factor α is determined; secondly, the PD signal is decomposed by the VMD algorithm to obtain K intrinsic mode functions (IMF). Then, wavelet threshold denoising is applied to each IMF, and the denoised IMFs are reconstructed. Finally, the feasibility of the denoising method proposed in this paper is verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The system mainly completes operations through elevators and multiple rail-guided vehicles (RGVs). This paper focuses on the scheduling optimization of compound operations in the AVS/RS to improve system performance. Compound operations involve the coordinated execution of both single-command and double-command operations. A mathematical model with compound operations was proposed and effectively decomposed into a horizontal component for RGVs and a vertical counterpart for the elevator, which can represent the operations of one elevator cooperating with multiple RGVs. The goal of this model was to minimize the makespan for compound operations and to determine the optimal operation sequence and path for RGVs. An improved discrete particle swarm optimization (DPSO) algorithm called AGDPSO was proposed to solve the model. The algorithm combines DPSO and a genetic algorithm in an adaptive manner to prevent the algorithm from falling into local optima and relying solely on the initial solution. Through rigorous optimization, optimal parameters for the algorithm were identified. When assessing the performance of our improved algorithm against various counterparts, considering different task durations and racking configurations, our results showed that AGDPSO outperformed the alternatives, proving its effectiveness in enhancing system efficiency for the model. The findings of this study not only contribute to the optimization of AVS/RS but also offer valuable insights for designing more efficient warehouses. By streamlining scheduling, improving operations, and leveraging advanced optimization techniques, we can create a more robust and effective storage and retrieval system. [ABSTRACT FROM AUTHOR]

Rational scheduling of locomotive paths (the locus of the locomotive point in the train working diagram) is an important step in drawing a locomotive working diagram by a computer. But there are some problems in this process, such as the computer usually drawing a locomotive path that overlaps with another locomotive path (in the circumstances, the actual users of the locomotive working diagrams often misread the locomotive planning). At present, there are many studies about assigning sets of locomotives to each train in a preplanned train schedule; in contrast, the studies of visualizing the locomotive planning are relatively rare. Through investigating the locomotive working diagram users, this paper points out that the layout of locomotive paths should put the distance between lines being as large as possible and should put the number of the intersection between lines being as few as possible as the optimization aim which is based to solve the problem of the lines overlap or the problem of the lines beyond the margins for drawing the locomotive paths. This paper also builds the optimization model of locomotive working diagram layout. Based on determining the position of locomotive paths which can be delineated, a genetic algorithm is used to solve the optimizing model of locomotive working diagram layout in this paper. An example of a train working diagram with 36 trains is given at the end of the paper, which indicates that the optimization model of locomotive working diagram layout can better solve the problem of locomotive planning visualization. [ABSTRACT FROM AUTHOR]

This paper deals with the multiobjective optimization problem of an agro- alimentary production workshop. Three criteria are considered in addition to this initial cost of production: the cost of the out-of-date products, the cost of the distribution discount and the makespan, and a new coding is proposed for this type of workshop. The adopted approach consists in generating optimal solutions diversified in the search space of solutions, and to help the decision maker when it cannot give a particular preference to one of the objective functions to make the good decision with respect to the quoted criteria. [ABSTRACT FROM AUTHOR]

Due to the characteristics of online learning resource recommendation such as large scale, uneven quality and diversity of preferences, how to accurately obtain various personalized learning resource lists has become an urgent problem to be solved in the field of online learning resource recommendation. This paper proposes an online learning resource recommendation model based on the improved NSGA-Ⅱ algorithm, which integrates the Tabu search algorithm to improve the local search ability of NSGA-Ⅱ algorithm. It takes background fitness, cognitive fitness and diversity as the objective functions for optimization. The dynamic updating of crowding degree is used to avoid the risk that the individuals with low crowding degree in the same area are deleted at the same time. Meanwhile, an adaptive genetic algorithm is applied to assign the optimal crossover rate and the mutation rate according to individual adaptability level, which ensures the convergence of genetic algorithm and the diversity of population. The experimental results show that the proposed model is superior to the traditional recommendation algorithm in terms of accuracy index, mean fitness, recall rate, F1 mean, HV, GD and IGD, etc., thus verifying the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Power system often experiences the problem of low-frequency electromechanical oscillations which leads the system to unstable condition. The problem can be corrected by implementing power system stabilizers (PSSs) in the excitation control system of alternator. This paper provides a novel and efficient approach to design an Improved Grasshopper Optimization Algorithm (IGOA)-based dual-input controller to damp the inter-area-mode power system oscillations. A three-fold optimization criterion has been formulated to calculate the optimum values of the controllers required for power system stability. The damping performance of the proposed controller is compared with conventional PSS and genetic algorithm-based controllers to validate the better performance of the proposed IGOA-based controller under various system loading conditions and disturbances. [ABSTRACT FROM AUTHOR]

This paper proposes a high-satisfaction evaluation method for health-care product design based on the genetic optimization algorithm in order to deeply understand consumers' satisfaction with forest health-care products. This article builds a consumer demand index system for the service content of forest health-care bases from the four levels of environmental conditions, service items, supporting facilities, and service levels. According to the questionnaire survey results of 1000 randomly selected forest health-care consumers, based on the genetic optimization algorithm, the overall design takes the four levels of satisfaction evaluation into account, resulting in a more reasonable evaluation of health-care products. Experiments show that the method proposed in this paper outperforms the traditional method for evaluating health-care products. [ABSTRACT FROM AUTHOR]

Optimization of land surface models has been very challenging due to the increasing complexity of such models. Typical parameter calibration techniques often limit the solution of the spatiotemporal discrepancy in the modeling performance levels especially for regional applications. Thus, in this study, an attempt was made to perform schemebased model optimization by designing a framework for coupling a micro-genetic algorithm (micro-GA) with the Noah land surface model that has multiple physics options (Noah-MP). Micro-GA controls the scheme selections in 10 different land surface parameterization fields in Noah-MP in order to extract the optimal scheme combination for a certain region. This coupling framework was successfully applied to the optimization of the surface water partitioning in the Korean Peninsula, promising not only the effectiveness of the scheme-based optimization but also model diagnosis capability by exploring the scheme sensitivity during the micro-GA evolution process. Then, the method was applied to four different regions in East Asia that have different climatic characteristics. The results indicate that (1) the optimal scheme combinations vary with the regions, (2) schemes related to the surface water partitioning are important for the modeling accuracy, and (3) specialized post-parameter optimization for each region may be required. [ABSTRACT FROM AUTHOR]

Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges. [ABSTRACT FROM AUTHOR]

In this paper, different methods are utilized to solve the coordination issue involving directional overcurrent relays (DOCRs) and distance relays. The proper coordination of DOCRs and distance relays is a critical issue for system security in electrical networks. Finding the DOCRs setting, pickup current(Ip) and timed dial setting (TDS), and operating time for zone-2 of distance relays is the primary objective of solving the coordination problem. The constant parameters A & B of the directional overcurrent relay that are responsible to control the form of the relay´s characteristics as well as the Ip and TDS have been regarded as variables in this problem. The optimal value for these four DOCR settings has been determined using different optimization techniques. The primary and backup relays must operate sequentially and without any violations, and this must be guaranteed by optimization techniques. In order to determine the operation time for zone-2 and DOCRs setting, optimization methods are examined utilizing the 8-bus and IEEE 30-bus networks. Different optimization algorithms, including recent and traditional techniques, are compared. The obtained results show the superiority of the genetic algorithm (GA) in solving the coordination problem of distance relays and DOCRs. Also, the obtained results prove the ability of the GA method compared to the particle swarm algorithm (PSO), grey wolf optimization (GWO), water cycle technique (WCA), equilibrium optimizer (EO), African vultures optimization algorithm (AVOA), flow direction algorithm (FDA), and gorilla troops optimizer (GTO) techniques. [ABSTRACT FROM AUTHOR]

Radial basis function networks are widely used in a multitude of applications in various scientific areas in both classification and data fitting problems. These networks deal with the above problems by adjusting their parameters through various optimization techniques. However, an important issue to address is the need to locate a satisfactory interval for the parameters of a network before adjusting these parameters. This paper proposes a two-stage method. In the first stage, via the incorporation of grammatical evolution, rules are generated to create the optimal value interval of the network parameters. During the second stage of the technique, the mentioned parameters are fine-tuned with a genetic algorithm. The current work was tested on a number of datasets from the recent literature and found to reduce the classification or data fitting error by over 40% on most datasets. In addition, the proposed method appears in the experiments to be robust, as the fluctuation of the number of network parameters does not significantly affect its performance. [ABSTRACT FROM AUTHOR]

In the realm of bioinformatics, Multiple Sequence Alignment (MSA) is a pivotal technique used to optimize the alignment of multiple biological sequences, guided by specific scoring criteria. Existing approaches addressing the MSA challenge tend to specialize in distinct biological features, leading to variability in alignment outcomes for the same set of sequences. Consequently, this paper proposes an enhanced evolutionary-based approach that simplifies the sequence alignment problem without considering the sequences in the non-dominated solution. Our method employs a multi-objective optimization technique that uniquely excludes non-dominated solution sets, effectively mitigating computational complexities. Utilizing the Sum of Pairs and the Total Conserved Column as primary objective functions, our approach offers a novel perspective. We adopt an integer coding approach to enhance the computational efficiency, representing chromosomes with sets of integers during the alignment process. Using the SABmark and BAliBASE datasets, extensive experimentation is conducted to compare our method with existing ones. The results affirm the superior solution quality achieved by our approach compared to its predecessors. Furthermore, via the Wilcoxon signed-rank test, a statistical analysis underscores the statistical significance of our model's improvement (p < 0.05). This comprehensive approach holds promise for advancing Multiple Sequence Alignment in bioinformatics. [ABSTRACT FROM AUTHOR]

Artificial neural networks are widely established models of computational intelligence that have been tested for their effectiveness in a variety of real-world applications. These models require a set of parameters to be fitted through the use of an optimization technique. However, an issue that researchers often face is finding an efficient range of values for the parameters of the artificial neural network. This paper proposes an innovative technique for generating a promising range of values for the parameters of the artificial neural network. Finding the value field is conducted by a series of rules for partitioning the original set of values or expanding it, the rules of which are generated using grammatical evolution. After finding a promising interval of values, any optimization technique such as a genetic algorithm can be used to train the artificial neural network on that interval of values. The new technique was tested on a wide range of problems from the relevant literature and the results were extremely promising. [ABSTRACT FROM AUTHOR]

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model's performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization. [ABSTRACT FROM AUTHOR]

In this paper, a system for LTE Cell Outage Compensation (COC) based on hybrid Genetic Algorithms (GA) and Artificial Neural Networks (ANN) has been proposed. COC aims to minimize the impact of cell outage which leads to decrease in operator revenue and/or the customer satisfaction. The proposed system adopts an optimization module to search for an optimal setting of a set of LTE operational parameters to achieve a targeted set of key performance indicators. The optimization process always leads to good enough solutions, but it also requires a huge number of trials. So, in the proposed system, a huge set of outage scenarios is collected along with their optimal argument settings that are acquired by the optimization module and they are used to train an artificial neural network (ANN) module, which acts as an expert that can optimally act on the different situations in real‐time mode. Simulation environment is set to evaluate different LTE measures and Key Performance Indicators (KPIs) on different outage scenarios. Simulation results proved the capability and robustness of the proposed system to minimize the number of users experiencing outage. Simulation results also show that the proposed system achieves optimal parameter settings without violating the overall system performance and with minimal processing time, while introducing significant impact on the performance of LTE. [ABSTRACT FROM AUTHOR]

With the emergence of more and more computing-intensive and latency-sensitive applications, insufficient computing power and energy of user devices has become a common phenomenon. Mobile edge computing (MEC) is an effective solution to this phenomenon. MEC improves task execution efficiency by offloading some tasks to edge servers for execution. In this paper, we consider a device-to-device technology (D2D)-enabled MEC network communication model, and study the subtask offloading strategy and the transmitting power allocation strategy of users. The objective function is to minimize the weighted sum of the average completion delay and average energy consumption of users, which is a mixed integer nonlinear problem. We first propose an enhanced particle swarm optimization algorithm (EPSO) to optimize the transmit power allocation strategy. Then, we utilize the Genetic Algorithm (GA) to optimize the subtask offloading strategy. Finally, we propose an alternate optimization algorithm (EPSO-GA) to jointly optimize the transmit power allocation strategy and the subtask offloading strategy. The simulation results show that the EPSO-GA outperforms other comparative algorithms in terms of the average completion delay, average energy consumption, and average cost. In addition, no matter how the weight coefficients of delay and energy consumption change, the average cost of the EPSO-GA is the least. [ABSTRACT FROM AUTHOR]

This paper represents the execution of a reliable, steady and wideband fractional order digital integrator (FODI) in respect of infinite impulse response (IIR) filter using a recent nature inspired meta-heuristic optimization technique known as mayfly optimization algorithm (MOA). MOA significantly functions stronger against some of the popular algorithms like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), improved particle swarm optimization (IPSO), whale optimization algorithm (WOA) and chaotic oppositional whale optimization algorithm (COWOA) with reference to different magnitude error performances, explanation character accuracy, convergence profile and computational time required to find the optimal solution. In this paper, the authors thoroughly analyze the convergence behavior and magnitude error metrices of the different order FODI's using WOA, COWOA and MOA. MATLAB simulation results also powerfully uphold the magnitude response efficiency and stability of the proposed different order FODI's over some recently published research papers. [ABSTRACT FROM AUTHOR]

In this paper, rainfall droplet impact force is transformed into a measurable voltage signal output via the piezoelectric material direct effect utilized for sensing purposes. The motivating sensor is utilized to measure the peak impact forces of rainfall droplets for further analysis and processing. Constructing a sense for the impact force of rainfall droplets has great implications in many real-life applications that can provide vital information regarding the amplifications of the impact force of rainfall on soil erosion, and the impact on small creatures and plants, etc. The rainfall droplet is set to collide on a very thin aluminum plate with negligible mass that can be presented geometrically as an extended segment of the proposed sensing device. The proposed sensing device is composed of a bimorph simply supported composite-piezoelectric beam that buckles due to the effect of the rain droplets' vertical impact force. The proposed device is designed for optimal performance in terms of the amount of voltage that can be measured. This is accomplished by having the first critical buckling load of the device as less than the impact force of the rainfall droplet. Accordingly, the well-known genetic algorithm (GA) automated optimization technique is utilized in this paper to enhance the measured voltage signal. A proof mass is added to the middle of the beam to amplify the magnitude of the measured voltage signal. The voltage signal is intended to be transferred to the PC via a data acquisition system. The rainfall droplets' peak impact forces are obtained analytically due to the nonlinear behavior of the beam using the Euler–Bernoulli thin beams assumptions. The FE model using COMSOL 6.0 Multiphysics commercial software is used to verify the analytical results. [ABSTRACT FROM AUTHOR]

By triggering the switches in sequence and controlling the brick charging voltage independently, a pulsed power system can generate flexible current waveform in the load, which could allow high-accurate magnetic pressure in experimental materials in the study of EOS data and dynamic response of material characterization. Currently, a 1.5-MA driver with 20 independent bricks has been developed, and a programmable 256-brick parallel discharge driver which is able to generate shaped current greater than 6.5 MA is under construction in the China Academy of Engineering Physics. In this paper, for these two multibrick parallel discharge drivers, the method of optimizing the output current waveform of the driver is studied by using the genetic algorithm and the corresponding optimization results of different target current waveforms are obtained. An experimental platform is set up for studying the gas switch breakdown characteristics. The self-breakdown and triggering breakdown experiments of the switch are conducted, and its breakdown characteristics are researched. Meanwhile, the effect of breakdown delay and jitter on the output current waveform is studied by simulation. This paper is of great significance to the actual operation of the multibrick parallel discharge drivers. [ABSTRACT FROM AUTHOR]

An improved matrix synthesis approach for inline filter is presented in this paper. Frequency-variant couplings (FVC) can generate and control multiple finite transmission zeros (TZs). As the resultant network only involves resonators cascaded one by one without any auxiliary elements (such as cross-coupled or extracted-pole structures), this paper provides the best optimizatised synthesis solution in configuration simplicity for narrowband filters based on genetic algorithm (GA) and solvopt optimization method. Compared with the conventional synthesis method for inline topology filters, the method presented in this paper has following advantages: First, it is unnecessary to consider both the couplings and capacitances of a traditional low-pass prototype. Second, there is no need to use similar transformation, and the adjacent FVCs can be implemented. Third, the approach presented can implement more TZs than the previous works. The maximum number of TZs can be as many as the filter order. Two examples with different topologies and specifications are synthesized to show the validation of the method presented in this paper. [ABSTRACT FROM AUTHOR]

The prediction of cyber security situation plays an important role in early warning against cyber security attacks. The first-order accumulative grey model has achieved remarkable results in many prediction scenarios. Since recent events have a greater impact on future decisions, new information should be given more weight. The disadvantage of first-order accumulative grey models is that with the first-order accumulative method, equal weight is given to the original data. In this paper, a fractional-order cumulative grey model (FAGM) is used to establish the prediction model, and an intelligent optimization algorithm known as particle swarm optimization (PSO) combined with a genetic algorithm (GA) is used to determine the optimal order. The model discussed in this paper is used for the prediction of Internet cyber security situations. The results of a comparison with the traditional grey model GM(1,1), the grey model GM(1,n), and the fractional discrete grey seasonal model FDGSM(1,1) show that our model is suitable for cases with insufficient data and irregular sample sizes, and the prediction accuracy and stability of the model are better than those of the other three models. [ABSTRACT FROM AUTHOR]

The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging. [ABSTRACT FROM AUTHOR]

A multi-offspring genetic algorithm based on sorting grouping selection and combination pairing crossover was proposed in this paper. First, individuals in the population were sorted according to their objective function values, and the remaining individuals were divided into several groups after eliminating the worst. Then, the selection operation, which has the advantage of a simplified calculation that can be easily performed, was implemented. Second, a combination pairing crossover operator was designed. Individuals from different groups were selected for new combinations. In a combination, if a crossover condition was met, individuals pairing and transposons crossover operation were made. Otherwise, offspring copies of the individuals in the combination were used, which increase the opportunity of generating better offspring by producing multi-offspring. Finally, a new population was formed by adopting basic bit mutation operator, elitism policy and the strategy of multi-offspring population competition. Moreover, the catastrophe mechanism has been introduced into improved algorithm to avoid premature convergence. The test results on the functions of CEC 2017 test suites shown that the algorithm proposed in this paper has better search performance, stability and faster convergence to the global optimal solution. These results thus verified the effectiveness and feasibility of the algorithm proposed in this paper. Applying the improved algorithm to the location optimization problem of agricultural product logistics facilities, it shown that the improved algorithm is an effective method to solve the location optimization. [ABSTRACT FROM AUTHOR]

Improving the stability of power systems using FACT devices is an important and effective method. This paper uses a static synchronous series compensator (SSSC) installed in a power system to smooth out inter-area oscillations. A meta-heuristic optimization method is proposed to design the supplementary damping controller and its installation control channel within the SSSC. In this method, two control channels, phase and magnitude have been investigated for installing a damping controller to improve maximum stability and resistance in different operating conditions. An effective control channel has been selected. The objective function considered in this optimization method is multi-objective, using the sum of weighted coefficients method. The first function aims to minimize the control gain of the damping controller to the reduction of control cost, and the second objective function moves the critical modes to improve stability. It is defined as the minimum phase within the design constraints of the controller. A hybrid of two well-known meta-heuristic methods, the genetic algorithm (GA) and grey wolf optimizer (GWO) algorithm have been used to design this controller. The proposed method in this paper has been applied to develop a robust damping controller with an optimal control channel based on SSSC for two standard test systems of 4 and 50 IEEE machines. The results obtained from the analysis of eigenvalues and nonlinear simulation of the power system study show the improvement in the stability of the power system as well as the robust performance of the damping in the phase control channel. [ABSTRACT FROM AUTHOR]

Accurately predicting power consumption is essential to ensure a safe power supply. Various technologies have been studied to predict power consumption, but the prediction of power consumption using deep learning models has been quite successful. However, in order to predict power consumption by utilizing deep learning models, it is necessary to find an appropriate set of hyper-parameters. This introduces the problem of complexity and wide search areas. The power consumption field should be accurately predicted in various distributed areas. To this end, a customized consumption prediction deep learning model is needed, which is essential for optimizing the hyper-parameters that are suitable for the environment. However, typical deep learning model users lack the knowledge needed to find the optimal values of parameters. To solve this problem, we propose a method for finding the optimal values of parameters for learning. In addition, the layer parameters of deep learning models are optimized by applying genetic algorithms. In this paper, we propose a hyper-parameter optimization method that solves the time and cost problems that depend on existing methods or experiences. We derive a hyper-parameter optimization plan that solves the existing method or experience-dependent time and cost problems. As a result, the RNN model achieved a 30% and 21% better mean squared error and mean absolute error, respectively, than did the arbitrary deep learning model, and the LSTM model was able to achieve 9% and 5% higher performance. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for the design optimization of geometric nonlinearities developed by active elements embedded in prestressable, statically indeterminant, truss-like aerospace structures for the purpose of attenuating their dynamic aeroelastic response under turbulent aerodynamic gust conditions. Dynamic aeroelastic responses are analyzed considering random power spectral density (PSD) gust with a continuous Davenport spectrum (DS) and tuned discrete gust (TDG) with a one-minus-cosine (OMC) wind excitation profiles. A genetic optimization algorithm (GA) is utilized to determine optimal prestress values through active element actuations for the purpose of tuning the geometric stiffness and, therefore, the modal response of the structure when exposed to gust excitations. In addition, a new simplified control metric for comparing active member locations is proposed. A case study is analyzed with this methodology to minimize the pointing error of a simplified antenna structure. Pointing error attenuations of 22.1% and 17.0% were found for the structure under DS mean wind speeds of 889 (349.95) and 2,778 cm/s (1,093.61 in./s), respectively. Using the same two operating cases with the TDG excitation profile resulted in the overall pointing error to be reduced by 36.8% and 37.0%, respectively. The adaptive nature of the presented methodology allows a single actuator layout to mitigate structural response for a variety of load cases, which is a large benefit over many traditionally passive techniques. This paper expands the existing usage of geometric nonlinearities to determine optimal active element location and actuations for given optimization objectives under realistic environmental loading conditions. [ABSTRACT FROM AUTHOR]

Public resource scheduling refers to the rational allocation and effective use of resources, while public emergency scheduling refers to the rational allocation and effective use of resources in the context of emergencies. Its main purpose is to reduce casualties and property losses caused by emergencies. This paper mainly studies the emergency scheduling of public resources based on line sensing technology and solves the scheduling problem of public resources through algorithm optimization. Firstly, combined with the positioning algorithm of wireless sensor, this paper optimizes the positioning and detection technology of wireless sensor technology. Then, we design an improved multiagent genetic algorithm (MAGA-MTERS) using natural number coding and design a penalty function to solve the model. Then, the algorithm is compared with the traditional genetic algorithm. The results show that the accurate positioning of wireless sensor technology can improve the efficiency of public resource scheduling and save the scheduling cost. The multiagent genetic algorithm optimizes the positioning function of wireless sensor. Compared with the traditional genetic algorithm, MAGA-MTERS algorithm can obtain a better solution. [ABSTRACT FROM AUTHOR]

Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter's effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique. [ABSTRACT FROM AUTHOR]

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities. WOA-LFGA is tested with 29 mathematical optimization problems and a WSN coverage optimization model. Simulation results demonstrate that the improved algorithm is highly competitive compared with mainstream algorithms. Moreover, the practicality and the effectiveness of the improved algorithm in optimizing wireless sensor network coverage are confirmed. [ABSTRACT FROM AUTHOR]

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Air traffic controller (ATC) fatigue has become a major cause of air traffic accidents. Speech-based fatigue-state detection is proposed in this paper. The speech signal is preprocessed to further extract the Mel frequency cepstrum coefficient (MFCC) from speech discourse. The machine learning method is used in fatigue detection. However, single machine learning fatigue detection methods often have low detection accuracy. To solve this problem, an ensemble learning method based on self-adaption quantum genetic algorithm (SQGA) heterogeneous learning methods is proposed. Pattern-level and feature-level resampling are used to increase the differences in the base learner's training dataset. To enlarge the diversity of single learners, k-nearest neighbor (KNN), Bayesian network (BN), back propagation neural network (BPNN) and support vector machine (SVM) are adopted for the heterogeneous ensemble. On this basis, finally, the detection result is obtained by weighted summation. The weight of each base learner was determined by SQGA. The SQGA method combines the quantum genetic algorithm with the adaptive strategy. The adaptive strategy includes adaptive adjustment of the quantum rotation gate, adaptive generation of crossover probability and adaptive generation of mutation probability. The experiments on real civil aviation radio land–air communication show that the proposed method can obtain 98.5% detection accuracy, with a 1.2% false and 3.0% missing report rate, whereas the SVM only obtains 94.0% detection accuracy, with a 5.4% false and 9.0% missing report rate. [ABSTRACT FROM AUTHOR]

Considering that in the process of job scheduling, the cluster load should be prebalanced rather than remedied when the load is seriously unbalanced; therefore, in this paper, the task scheduling flow of the Hadoop cluster is analyzed deeply. On the Hadoop platform, a self-dividing algorithm is proposed for load balancing. An intelligent optimization algorithm is used to solve load balance. A dynamic feedback load balancing scheduling method is proposed from the point of view of task scheduling. In order to solve the shortcoming of the fair scheduling algorithm, this paper proposes two ways to improve the resource utilization and overall performance of Hadoop. When the mapping task is completed and the tasks to be reduced are assigned, the task assignment is based on the performance of the nodes to be reduced. It gives full play to the advantages of the ant colony algorithm and the hive colony algorithm so that the fusion algorithm can better deal with load balance. Then, three existing scheduling algorithms are introduced in detail: single queue scheduling, capacity scheduling, and fair scheduling. On this basis, an improved task scheduling strategy based on genetic algorithm is proposed to allocate and execute application tasks to reduce task completion time. The experiment verifies the effectiveness of the algorithm. The LBNP algorithm greatly improves the efficiency of reducing task execution and job execution. The delay capacity scheduling algorithm can ensure that most tasks can achieve localization scheduling, improve resource utilization, improve load balance, and speed up job completion time. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) inspection can be considered as a path planning problem for the UAVs, which needs UAVs to traverse all task points one by one by avoiding some obstacles. In most current researches, only single objective function is used in the path planning for the UAVs, and some other objectives are seldom considered together, like: the number of UAVs, the required inspection time and so on. Therefore, in order to overcome the defect of using single objective function to plan the path of UAVs, a many-objective optimization based multiple UAVs path planning method is studied in this paper. Firstly, four objective functions are chosen which include the flight distance, the flight stability, the number of UAVs and the time offset of reaching the task point; considering the arrival time of UAVs at each task point is an interval value in the practical application, the time offset of reaching the task point is set as an interval objective function, which is handled by the matter-element extension method in this paper. Then, an improved NSGA-III algorithm is proposed to solve the established many-objective optimization problem, which uses fruit fly optimization algorithm (FOA) to replace the genetic algorithm (GA) in the NSGA-III algorithm. Finally, several evaluation indictors (Spread, generational distance (GD), inverse generational distance (IGD), and running time) are used to choose the optimal flight paths of multiple UAVs. Through simulation comparisons with other algorithms, it is concluded that the improved NSGA-III algorithm is more effective in the path planning of UAVs. [ABSTRACT FROM AUTHOR]

Drones and drone swarms, characterized by their low price and ease of deployment, are being utilized to launch assaults, and presenting a great threat to the public and homeland security in recent years. Countering drones or drone swarms is thus of great significance. Some effective counter approaches for a small group of drones have been studied. However, these approaches may be insufficient to counter a large-scale drone swarm due to the lack of efficient time-saving detecting technologies. Towards this end, this paper proposes a fast counter approach to deprive the drone swarm of its coordination and clustering capabilities in a short time by splitting the drone swarm into several unconnected components. To achieve efficient splitting, two efficient algorithms for searching critical nodes are proposed, namely, the genetic algorithm and the particle swarm optimization algorithm. Extensive simulation results are presented to validate the superior performances of the proposed two algorithms for splitting the drone swarms in different formations. The results show that the drone swarms lose their coordination ability as they are forced to split into multiple components with a constraint of group size. The high accuracy and efficiency of the proposed algorithms are also verified by a series of comparative experiments. [ABSTRACT FROM AUTHOR]

In this paper, renewable resources, namely photovoltaic panels (PV), are placed in a specific configuration to obtain the maximum reliability and availability of a microgrid and study the subcomponent-level reliability and availability. The reliability of components can be increased by trying different configurations of the components. We identify the preferred configuration used for the PV panels as bridged linked. The overall reliability of the microgrid is increased when component-wise reliability is considered. Even components are further divided into subcomponents, and the multiple faults of each component are considered. The method used for the reliability evaluation and availability study is Markov state transition modeling. The microgrid's reliability and availability are plotted concerning time using Matlab. The optimization of reliability and availability is conducted through optimization techniques such as the genetic algorithm (GA) and artificial neural networks (ANN). The results are compared and validated for the optimal values of mean time to failure (MTTF) and mean time to repair (MTTR). Using a genetic algorithm, there is a 96% of improvement in the reliability, and after applying the neural networks, a significant improvement of 97% along with quick results is achieved. [ABSTRACT FROM AUTHOR]

This paper presents an efficient cooperative multi-populations swarm intelligence algorithm based on the Harris Hawks optimization (HHO) algorithm, named CMPMO-HHO, to solve multi-/many-objective optimization problems. Specifically, this paper firstly proposes a novel cooperative multi-populations framework with dual elite selection named CMPMO/des. With four excellent strategies, namely the one-to-one correspondence framework between the optimization objectives and the subpopulations, the global archive for information exchange and cooperation among subpopulations, the logistic chaotic single-dimensional perturbation strategy, and the dual elite selection mechanism based on the fast non-dominated sorting and the reference point-based approach, CMPMO/des achieves considerably high performance on solutions convergence and diversity. Thereafter, in each subpopulation, HHO is used as the single objective optimizer for its impressive high performance. Notably, however, the proposed CMPMO/des framework can work with any other single objective optimizer without modification. We comprehensively evaluated the performance of CMPMO-HHO on 34 multi-objective and 19 many-objective benchmark problems and extensively compared it with 13 state-of-the-art multi/many-objective optimization algorithms, three variants of CMPMO-HHO, and a CMPMO/des based many-objective genetic algorithm named CMPMO-GA. The results show that by taking the advantages of the CMPMO/des framework, CMPMO-HHO achieves promising performance in solving multi/many-objective optimization problems. [ABSTRACT FROM AUTHOR]

The research paper presents a novel approach toward constructing motion equations for structures with attached MTMDs (multiple tuned mass dampers). A primary system with MDOF (multiple dynamic degrees of freedom) was reduced to an equivalent system with a SDOF (single degree of freedom) through the modal approach, and equations from additional MTMDs were added to a thus-created system. Optimization based on ℌ2 and ℌ∞ for the transfer function associated with the generalized displacement of an SDOF system was applied. The research work utilized GA (genetic algorithms) and SA (simulated annealing method) optimization algorithms to determine the stiffness and damping parameters for individual TMDs. The effect of damping and stiffness (MTMD tuning) distribution depending on the number of TMDs was also analyzed. The paper also reviews the impact of primary system mass change on the efficiency of optimized MTMDs, as well as confirms the results of other authors involving greater MTMD effectiveness relative to a single TMD. [ABSTRACT FROM AUTHOR]

In order to improve the communication efficiency of the CAN bus application layer communication protocol of the inverter network monitoring system, this paper proposes a TTCAN scheduling optimization algorithm for the robot inverter remote monitoring system based on PLC and cloud platform. According to the analysis of the communication requirements of the frequency converter monitoring system, this paper designs the application layer protocol of the nodes in the system and establishes the system scheduling matrix. The time triggered can bus protocol (TTCAN) combines the event triggered mechanism with the time triggered mechanism. The hybrid scheduling strategy is used to optimize the system matrix of TTCAN; that is, the hybrid particle swarm optimization algorithm is used for periodic messages. For nonperiodic messages, uniform scheduling strategy and dynamic scheduling algorithm are adopted. The simulation is carried out by MATLAB tools. The simulation results show that the sum of partial minimum transmission time is 1928 and there are multiple optimal individuals through the hybrid particle swarm optimization algorithm. Compared with the traditional genetic algorithm and single particle swarm optimization algorithm, the hybrid algorithm is better than the traditional genetic algorithm and particle swarm optimization algorithm in terms of iteration times and average fitness value. In conclusion, the optimized TTCAN protocol improves the real-time performance, reliability, and bandwidth utilization of the communication network. [ABSTRACT FROM AUTHOR]