Search Results (24)

This article presents an algorithm for solving the direct and inverse problem for a model consisting of a fractional differential equation with non-integer order derivatives with respect to time and space. The Caputo derivative was taken as the fractional derivative with respect to time, and the Riemann–Liouville derivative in the case of space. On one of the boundaries of the considered domain, a fractional boundary condition of the third kind was adopted. In the case of the direct problem, a differential scheme was presented, and a metaheuristic optimization algorithm, namely the Group Teaching Optimization Algorithm (GTOA), was used to solve the inverse problem. The article presents numerical examples illustrating the operation of the proposed methods. In the case of inverse problem, a function occurring in the fractional boundary condition was identified. The presented approach can be an effective tool for modeling the anomalous diffusion phenomenon. Full article

Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved k-NN data mining algorithm. Firstly, we establish the naive Bayes machine learning algorithm to achieve accurate classification of the students in the class and then implement student grouping based on this accurate classification. Then, relying on the student grouping, we use the matching features between the students’ interest vector and the practical topic vector to construct an intelligent teaching recommendation model based on an improved k-NN data mining algorithm, in which the optimal complete binary encoding tree for the discussion topic is modeled. Based on the encoding tree model, an improved k-NN algorithm recommendation model is established to match the student group interests and recommend discussion topics. The experimental results prove that our proposed recommendation algorithm (PRA) can accurately recommend discussion topics for different student groups, match the interests of each group to the greatest extent, and improve the students’ enthusiasm for participating in practical discussions. As for the control groups of the user-based collaborative filtering recommendation algorithm (UCFA) and the item-based collaborative filtering recommendation algorithm (ICFA), under the experimental conditions of the single dataset and multiple datasets, the PRA has higher accuracy, recall rate, precision, and F1 value than the UCFA and ICFA and has better recommendation performance and robustness. Full article

This study addresses the sentiment classification of short texts in teaching evaluations. To mitigate concerns regarding data security in cloud-based sentiment analysis and to overcome the limited feature extraction capacity of traditional deep-learning methods, we propose a distributed symmetric approximate homomorphic hybrid sentiment classification model, denoted BHE+ALBERT-Mixplus. To enable homomorphic encryption of non-polynomial functions within the ALBERT-Mixplus architecture—a mixing-and-enhancement variant of ALBERT—we introduce the BHE (BERT-based Homomorphic Encryption) algorithm. The BHE establishes a distributed symmetric approximation workflow, constructing a cloud–user symmetric encryption framework. Within this framework, simplified computations and mathematical approximations are applied to handle non-polynomial operations (e.g., GELU, Softmax, and LayerNorm) under the CKKS homomorphic-encryption scheme. Consequently, the ALBERT-Mixplus model can securely perform classification on encrypted data without compromising utility. To improve feature extraction and enhance prediction accuracy in sentiment classification, ALBERT-Mixplus incorporates two core components: 1. A meta-information extraction layer, employing a lightweight pre-trained ALBERT model to capture extensive general semantic knowledge and thereby bolster robustness to noise. 2. A hybrid feature-extraction layer, which fuses a bidirectional gated recurrent unit (BiGRU) with a multi-scale convolutional neural network (MCNN) to capture both global contextual dependencies and fine-grained local semantic features across multiple scales. Together, these layers enrich the model’s deep feature representations. Experimental results on the TAD-2023 and SST-2 datasets demonstrate that BHE+ALBERT-Mixplus achieves competitive improvements in key evaluation metrics compared to mainstream models, despite a slight increase in computational overhead. The proposed framework enables secure analysis of diverse student feedback while preserving data privacy. This allows marginalized student groups to benefit equally from AI-driven insights, thereby embodying the principles of educational equity and inclusive education. Moreover, through its innovative distributed encryption workflow, the model enhances computational efficiency while promoting environmental sustainability by reducing energy consumption and optimizing resource allocation. Full article

The article presents research on the application of computed tomography with an incomplete dataset to the problem of examining the internal structure of walls. The case of incomplete information in computed tomography often occurs in various applications, e.g., when examining large objects or when examining hard-to-reach objects. Algorithms dedicated to this type of problem can be used to detect anomalies (defects, cracks) in the walls, among other artifacts. Situations of this type may occur, for example, in old buildings, where special caution should be exercised. The approach presented in the article consists of a non-standard solution to the problem of reconstructing the internal structure of the tested object. The classical approach involves constructing an appropriate system of equations based on X-rays, the solution of which describes the structure. However, this approach has a drawback: solving such systems of equations is computationally very complex, because the algorithms used, combined with incomplete information, converge very slowly. In this article, we propose a different approach that eliminates this problem. To simulate the structure of the tested object, we use a hybrid algorithm that is a combination of a metaheuristic optimization algorithm (Group Teaching Optimization Algorithm) and a numerical optimization method (Hook-Jeeves method). In order to solve the considered inverse problem, a functional measuring the fit of the model to the measurement data is created. The hybrid algorithm presented in this paper was used to find the minimum of this functional. This paper also shows computational examples illustrating the effectiveness of the algorithms. Full article

Mobile robots play a pivotal role in advancing smart manufacturing technologies. However, existing Obstacle avoidance path Planning (OP) algorithms for mobile robots suffer from low stability and applicability. Therefore, this paper proposes an enhanced Secret Bird Optimization Algorithm (SBOA)-based OP algorithm for mobile robots to address these challenges, termed AGMSBOA. Firstly, an adaptive learning strategy is introduced, where individuals enhance the diversity of the algorithm’s population by summarizing relationships among candidates of varying quality, thereby strengthening the algorithm’s ability to locate globally optimal obstacle avoidance path regions. Secondly, a group learning strategy is incorporated by dividing the population into learning and teaching groups, enhancing the algorithm’s exploitation capabilities, improving the accuracy of obstacle avoidance path planning, and reducing actual runtime. Lastly, a multiple population evolution strategy is proposed, which balances the exploration/exploitation phases of the algorithm by analyzing the nature of different individuals, improving the algorithm’s ability to escape suboptimal obstacle avoidance path traps. Subsequently, AGMSBOA was used to solve the OP problem on five maps and two OP problems in real-world environments. The experiments illustrate that AGMSBOA achieves more than 5% performance improvement in path length and a 100–win rate in runtime metrics, as well as faster convergence and stability of the solution. Therefore, AGMSBOA proposed in this paper is an efficient, robust, and robust OP method for mobile robots. Full article

Distributed interrupted sampling repeater jamming (D-ISRJ) is the application of interrupted sampling repeater jamming technology within the framework of distributed jamming systems. It can generate coherent false targets after passing through the target radar’s matched filter, but these false targets exhibit strong regularity in range and amplitude. Addressing this issue, a distributed interrupted sampling frequency-shifted repeater jamming method based on the group teaching optimization algorithm (GTOA) is proposed in this paper. By introducing frequency-shifted modulation during the retransmission of the jamming signal, the frequency shift amount of the jamming unit in each round of repeater jamming is used as an optimization variable to construct an optimization model for distributed interrupted sampling frequency-shifted repeater jamming. The parameters are then solved by using GTOA. Simulations are conducted to analyze the jamming effects under different distributed jamming modes, and the proposed optimization algorithm is compared to common swarm intelligence algorithms in the same optimization model. The method proposed in this paper can be used in the field of precision electronic warfare to improve the jamming effect of synthetic aperture radar. Experimental results show that under the given simulation conditions, the jamming signal generated by the proposed method can achieve better jamming effects. Full article

This research paper develops a novel hybrid approach, called hybrid particle swarm optimization–teaching–learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of “exploitation capabilities of PSO” is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, “exploration abilities of TLBO” means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications. Full article

To address the problems of the single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in the destruction process when the teaching–learning-based optimization (TLBO) algorithm solves the no-wait flow-shop-scheduling problem, the multi-strategy discrete teaching–learning-based optimization algorithm (MSDTLBO) is introduced. Considering the differences between individuals, the algorithm is redefined from the student’s point of view, giving the basic integer sequence encoding. To address the fact that the algorithm is prone to falling into local optimum and to leading to a reduction in search accuracy, the population was divided into three groups according to the learning ability of the individuals, and different teaching strategies were adopted to achieve the effect of teaching according to their needs. To improve the destruction-and-reconstruction process with symmetry, an iterative greedy algorithm of destruction–reconstruction was used as the main body, and a knowledge base was used to control the number of meaningless artifacts to be destroyed and to dynamically change the artifact-selection method in the destruction process. Finally, the algorithm was applied to the no-wait flow-shop-scheduling problem (NWFSP) to test its practical application value. After comparing twenty-one benchmark test functions with six algorithms, the experimental results showed that the algorithm has a certain effectiveness in solving NWFSP. Full article

Part of the widely discussed problem in electrical power systems is the optimal reactive power dispatch (ORPD) due to its reliability and economical operation of electrical power systems. The ORPD is a complex and nonlinear optimization problem. The pathfinder algorithm (PFA) is a newly developed algorithm that inspires the group movement of prey with a leader called a pathfinder when hunting for food. The inertia weight is added to the PFA and is called an improved pathfinder algorithm (IPFA) to support the proper random work of the swarm to avoid the decrease in searchability of the PFA. The IPFA was proposed in this work to diminish the active power loss while improving the voltage profile. The IPFA was validated on the IEEE 30 and 118 bus systems along with particle swarm optimization (PSO) and the teaching–learning-based optimizer (TLBO). The proposed IPFA provides the best result as the losses of the IEEE 30 and 118 test systems were reduced to 16.035 and 115.048 MW from the initial base of 17.89 and 132.86 MW, respectively. The losses of PSO and the TLBO were 16.1568 and 16.1607 MW for the IEEE 30 bus system, respectively, while for the IEEE 118 bus system, the PSO provided 117.9129 MW and the TLBO provided 118.0524 MW. The two test systems’ reduction percentages (%) were 10.37% and 13.41%, respectively. The results were compared with those of other algorithms in the literature, and the IPFA provided a superior result, thereby suggesting the superiority of IPFA methods in diminishing the power loss and improving the system’s voltage profile. Full article

The Curriculum Sequencing (CS) problem is a challenging task to tackle in the field of online teaching and learning system development. The current methods of education management might still possess certain drawbacks that would cause ineffectiveness and incompatibility of the whole system. A solution for achieving better user satisfaction would be to treat users individually and to offer educational materials in a customized way. Adaptive Curriculum Sequencing (ACS) plays an important role in education management system, for it helps finding the optimal sequence of a curriculum among various possible solutions, which is a typical NP-hard combinatorial optimization problem. Therefore, this paper proposes a novel metaheuristic algorithm named Group-Theoretic Particle Swarm Optimization (GT-PSO) to tackle the ACS problem. GT-PSO would rebuild the search paradigm adaptively based on the solid mathematical foundation of symmetric group through encoding the solution candidates, decomposing the search space, guiding neighborhood movements, and updating the swarm topology. The objective function is the learning goal, with additional intrinsic and extrinsic information from those users. Experimental results show that GT-PSO has outperformed most other methods in real-life scenarios, and the insights provided by our proposed method further indicate the theoretical and practical value of an effective and robust education management system. Full article

The good indoor light environment and comfort of the teaching space are very important for students’ physical and mental health. Meanwhile, China advocates energy conservation and emission reduction policies. However, in order to obtain lower building energy consumption, higher thermal comfort, and daylighting, architects use performance simulation software to repeatedly simulate and refine, which is time-consuming and difficult to obtain the best results from three performances. Given this problem, we constructed the design framework in the early stage of the architectural design of the teaching building. In the first stage of the framework, architects optimized the performance objectives of lighting, thermal comfort, and energy consumption, and performed a cluster analysis on the optimized non-dominated solution to provide a reference for the architect. In the second stage of the framework, architects used the data generated in the optimization process to train the BP neural network and use the trained BP neural network to predict the performance of the building. In this paper, we selected Nanjing Donglu Middle School as a case study. The optimization of the building performance was assessed by a genetic algorithm, generating 3000 sets of sample data during the optimization iteration. Then, we analyzed the non-dominated solution of the sample data through the method of cluster analysis and trained the BP neural network with the sample data as a data set. The prediction model with R-values of 0.998 in the training set and test set was obtained by repeatedly debugging the number of neurons in the BP neural network. Finally, five groups of design parameters were randomly selected and brought into the trained BP neural network, and the predictive value was close to the simulated value. The construction of the framework provides design ideas for architects in the early teaching of building design and helps designers to make better decisions. Full article

The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results. Full article

A growing number of researchers are interested in deploying unmanned surface vehicles (USVs) in support of ocean environmental monitoring. To accomplish these missions efficiently, multiple-waypoint path planning strategies for survey USVs are still a key challenge. The multiple-waypoint path planning problem, mathematically equivalent to the traveling salesman problem (TSP), is addressed in this paper using a discrete group teaching optimization algorithm (DGTOA). Generally, the algorithm consists of three phases. In the initialization phase, the DGTOA generates the initial sequence for students through greedy initialization. In the crossover phase, a new greedy crossover algorithm is introduced to increase diversity. In the mutation phase, to balance the exploration and exploitation, this paper proposes a dynamic adaptive neighborhood radius based on triangular probability selection to apply in the shift mutation algorithm, the inversion mutation algorithm, and the 3-opt mutation algorithm. To verify the performance of the DGTOA, fifteen benchmark cases from TSPLIB are implemented to compare the DGTOA with the discrete tree seed algorithm, discrete Jaya algorithm, artificial bee colony optimization, particle swarm optimization-ant colony optimization, and discrete shuffled frog-leaping algorithm. The results demonstrate that the DGTOA is a robust and competitive algorithm, especially for large-scale TSP problems. Meanwhile, the USV simulation results indicate that the DGTOA performs well in terms of exploration and exploitation. Full article

Due to its significance in the operation of power systems, the optimal power flow (OPF) problem has attracted increasing interest with the introduction of smart grids. Optimal power flow developed as a crucial instrument for resource planning effectiveness as well as for enhancing the performance of electrical power networks. Transmission line losses, total generation costs, FACTS (flexible alternating current transmission system) costs, voltage deviations, total power transfer capability, voltage stability, emission of generation units, system security, etc., are just a few examples of objective functions related to the electric power system that can be optimized. Due to the nonlinear nature of optimal power flow problems, the classical approaches may become locked in local optimums, hence, metaheuristic optimization techniques are frequently used to solve these issues. The most recent optimization strategies used to solve optimal power flow problems are discussed in this paper as the state of the art (according to the authors, the most pertinent studies). The presented optimization techniques are grouped according to their sources of inspiration, including human-inspired algorithms (harmony search, teaching learning-based optimization, tabu search, etc.), evolutionary-inspired algorithms (differential evolution, genetic algorithms, etc.), and physics-inspired methods (particle swarm optimization, cuckoo search algorithm, firefly algorithm, ant colony optimization algorithm, etc.). Full article

Nowadays, wind energy is receiving considerable attention due to its availability, low cost, and environment-friendly operation. Wind turbines are rarely placed individually but rather in the form of a wind farm with a group of several wind turbines. The purpose of this research is to perform studies on wind turbine farms in order to find the best distribution for wind turbines that maximizes the produced power, hence minimizing the wind farm area. Wind Farm Area Optimization (WFAO) is performed for optimal placement of wind turbines using elitist teaching–learning-based optimization (ETLBO) techniques. Three different scenarios of wind (first is fixed wind direction and constant speed, second is variable wind direction and constant speed, and third is variable wind direction and variable speed) are considered to find the optimal number of turbines and turbine positioning in a minimized squared land area that maximizes the power production while minimizing the total cost. Other research carried out in the past was to find the optimal placement of the wind turbines in a fixed squared land area of $2 \mathrm{km}\times 2 \mathrm{km}$. In the present study, WFAO–ETLBO algorithm has been implemented to get the optimal land area for the placement of the same number of turbines used in the past research. For Case 1, there is a significant reduction in land area by approximately 30.75%, 45.25%, and 51.75% for each wind scenario, respectively. For Case 2, the reductions in land area for three different wind scenarios are respectively 30.75%, 7.2%, and 7.2%. For Case 3, there is a reduction of 7.2% in land area for each wind scenario. It has been observed that the results obtained by the WFAO–ETLBO algorithm with a significant reduction in the land area along with optimal placement of wind turbines are better than the results obtained from the wind turbines placement in the fixed land area of $2 \mathrm{km}\times 2 \mathrm{km}$. Full article