Hybrid Intelligent Algorithm Research Articles

Research on the Reform and Innovation of College Music Teaching Based on Collaborative Filtering Algorithm

With the rapid development of artificial intelligence technology, collaborative filtering (CF) algorithms are becoming increasingly widely used in the field of education. This paper aims to explore the application of CF algorithms in the reform and innovation of music teaching in universities, in order to improve teaching effectiveness and students’ learning experience. First, this paper analyzes the problems existing in current music teaching in universities, such as single teaching methods and low student participation. In order to complete the research on the extension of online music systems in music teaching, this paper reused the post filtering paradigm of contextual information and redesigned the two-stage process of the hybrid algorithm. After the initial screening of CF, the algorithm extracts recommendation results with contextual bias based on tag information. Then, based on the improved algorithm, an online music teaching system was implemented. Applying hybrid algorithm intelligent algorithms to university music teaching can not only improve the personalization and interactivity of teaching, but also promote the cultivation of students’ creativity and critical thinking abilities. The experimental results show that the music teaching system using hybrid intelligent algorithms significantly improves students’ learning participation, with an average participation rate increased by 30% compared to traditional teaching methods. At the same time, students’ learning outcomes have also significantly improved, with an average score increase of 15% in music theory exams. And in the assessment of creativity and critical thinking ability, students show stronger thinking activity and innovation ability. In addition, system feedback indicates that over 90% of students are satisfied with the teaching system, believing that it enhances the fun and interactivity of learning.

Research on the application of a hybrid intelligent algorithm in reducing the negative response of shield tunneling construction

Traditional shield project control relies on manual monitoring and management, which suffers from poor reliability, lag, and other defects. In this paper, a hybrid algorithm based on Bayesian optimization (BO), light gradient enhancer (LGBM), and nondominated sorting genetic algorithm-III (NSGA-III) is proposed to realize intelligent control of shield construction safety. The BO-LGBM-NSGA-III is a multiobjective optimization algorithm that takes cutterhead wear and shield energy consumption as objective functions and the nonlinear mapping function of surface settlement and construction parameters as fitness functions. Based on the Pareto distribution of the optimized backing structure construction parameters, reasonable limits of the control parameters of the shield tunneling machine are obtained. The conclusions are as follows. (1) The R2 of the test set for surface subsidence prediction is 0.916, and the RMSE is 2.655. (2) The proposed method can achieve multiobjective optimization, and the surface settlement, cutter head wear, and shield energy consumption are optimized by 49.92%, 24.38%, and 41.88%, respectively. (3) By using this method, both prediction and optimization can be realized, and the optimal control scheme can be obtained. The negative impact of shield tunneling construction has been significantly reduced.

Steam Pressure Control of Marine Boiler Based on AFSA-PSO-LSSVM

Abstract To solve the problem that the main steam pressure fluctuates greatly when the load of the combustion control system of the marine main boiler changes suddenly, present a combined inverse control system of Support vector machine and PID to control the steam pressure intelligently. The inverse model of the nonlinear system identified by LSSVM is used as the feedforward controller and the PID controller is used as the feedback controller. The hybrid intelligent algorithm of artificial fish swarm Algorithm and particle swarm optimization is used to optimize the parameters of the control system. The AFSA algorithm is used to search the initial values of the parameters and the PSO algorithm is locally updated. Finally, the mechanism of the steam pressure control system of the marine boiler is modeled and simulated, and the algorithm models of AFSA-PSO-LSSVM-PID, AFSA-LSSVM-PID, PSO-LSSVM-PID and LSSVM-PID are established. The experimental results show that the AFSA-PSO-LSSVM-PID model has a good dynamic, stability, anti-interference ability and robustness.

Hybrid intelligent algorithm aided energy consumption optimization in smart grid systems with edge computing

The rapid proliferation of smart grid systems necessitates efficient management of energy resources, particularly in the context of mobile edge computing (MEC) networks. This paper presents a novel approach to optimize the energy consumption in smart grid systems with the integration of edge computing, employing a hybrid intelligent algorithm (HIA) empowered by particle swarm optimization (PSO). The primary objective is to enhance the sustainability and operational efficiency of smart grid infrastructures by minimizing the energy consumption in the MEC networks. The proposed HIA utilizes PSO to dynamically allocate computational tasks and manage resources among edge devices based on real-time demand fluctuations. This adaptive approach aims to achieve the optimal load balancing and energy efficiency across the smart grid ecosystem. By leveraging the PSO’s ability to iteratively refine solutions and adapt to changing environmental conditions, the algorithm optimizes the energy consumption while maintaining requisite service levels and reliability. Simulation experiments and case studies validate the effectiveness of the proposed PSO-based HIA in reducing the energy consumption without compromising system other performances. The results demonstrate substantial improvements in the energy efficiency, illustrating the feasibility and benefits of employing intelligent algorithms tailored for edge computing environments within smart grid systems. This research contributes to advancing sustainable smart grid technologies by introducing a robust framework for energy optimization through hybrid intelligent algorithms.

Maximum power point tracking controller for photovoltaic system based on chaos quantum particle swarm optimization – moth-flame optimization hybrid model

To convert photovoltaic arrays to solar energy in a more efficient way, this paper has proposed a maximum power point tracking controller model based on the chaotic quantum particle swarm-mothballing hybrid algorithm. First, the optimization of the particle swarm algorithm is designed to solve defects, such as premature maturity by using the quantum and chaotic strategies. The mothballing algorithm is introduced to help the model find global optimization-seeking more quickly. After that, further optimization was made to operate the tracking model in both offline and real-time parameters. The conductivity increment method and the perturbation observation method were adopted to effectively track the model under different temperatures and light intensities. Finally, the simulation and analysis experiments were carried out on the Simulink platform. The study’s proposed maximum power point tracking controller achieved a steady-state accuracy η2 of 99.84%. In summary, the study has proposed a hybrid intelligent algorithm with extraction of internal parameters. The maximum power point tracker based on the proposed method is proved to be both effective and accurate.

Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm

In this paper, a hybrid intelligent framework comprising Bayesian optimization (BO), gradient boosting with categorical features (CatBoost) and the nondominated sorting genetic algorithm-III (NSGA-III) was proposed to support multiobjective optimization of shield construction parameters without large sample datasets, improve the shield performance, and ensure reliable and interpretable results. First, with the use of the specific tunneling energy, advancing speed and cutter wear as objective functions, a BO-CatBoost prediction model for shield construction parameters and various objectives was constructed, and the key influencing factors were identified via the SHapley Additive exPlanations (SHAP) method. Then, a BO-CatBoost-NSGA-III model was developed to obtain Pareto solutions under different scenarios involving the adjustment of the key influencing factors. Finally, adopting the Wuhan Metro as the background, the accuracy, stability, and generalizability of the constructed algorithm were verified. The results indicated that (1) the developed BO-CatBoost algorithm is superior to 9 other algorithms. The R2 values of the proposed approach were 0.976 and 0.901–0.976 on the test set. (2) The developed BO-CatBoost-NSGA-III algorithm could be used to obtain Pareto solutions under different scenarios via the adjustment of the key influencing factors with the SHAP method, and the optimal solutions could facilitate improvements in the advancing speed, specific tunneling energy and cutter wear of 3.45 %, 6.09 %, and 0.52 %, respectively, with an overall average reliability of 90.5 %. (3) By comparing various prediction algorithms, optimization schemes of different objectives and geological conditions, the accuracy, stability, and generalizability of the constructed algorithm were verified. The developed BO-CatBoost-NSGA-III framework could enable dynamic adjustment of shield construction parameters for decision-making purposes in the event of conflicting shield construction objectives and exhibits generality.

Smart grid management: Integrating hybrid intelligent algorithms for microgrid energy optimization

The increasing global demand for power drives the need for advancements in generation, control, and supply technologies. While bulk power generation remains cost-effective, it depends on transmission and distribution systems for delivery. Integrating distributed generators into the grid enhances reliability and direct power supply to consumers. Micro Grids (MGs) are a promising solution, offering smoother and more reliable operations. This study explores MGs, incorporating the latest loads and distributed generators to minimize operating costs for electrical power providers. This work utilizes intelligent algorithms, including the Firefly algorithm, Spider Monkey Optimization (SMO), and a hybrid approach combining both. The Firefly algorithm, inspired by swarm intelligence, mimics fireflies' light emission to optimize solutions. SMO, based on spider monkeys' social behavior, balances exploration and exploitation to achieve optimal results. A novel hybrid algorithm is developed to minimize MG operating costs by combining SMO's exploration with the Firefly algorithm's neighborhood optimization; enhancing navigation through the complex constraint solution space of MGs. Case studies on a modified IEEE 33-bus test feeder are conducted. The study compares three metaheuristic methods with non-convex solution spaces to MG operational algorithms. The results demonstrate the hybrid method's efficacy in determining the optimal location and size of real and reactive power support devices for MG operation. Numerical results show the hybrid algorithm's superiority over existing methods, reducing operating costs by 7.5 %, real power loss by 36.5 %, and reactive power loss by 1.7 %, and voltage deviation by 16.895. By considering MG constraints, the hybrid algorithm consistently delivers better outcomes, optimizing operating costs and ensuring efficient power supply. This study presents a novel MG optimization approach, enhancing future energy systems with improved solutions.

Prediction of white blood cell count during exercise: a comparison between standalone and hybrid intelligent algorithms

Decades of research in exercise immunology have demonstrated the profound impact of exercise on the immune response, influencing an individual's disease susceptibility. Accurate prediction of white blood cells (WBCs) count during exercise can help to design effective training programs to maintain optimal the immune system function and prevent its suppression. In this regard, this study aimed to develop an easy-to-use and efficient modelling tool for predicting WBCs count during exercise. To achieve this goal, the predictive power of a range of machine-learning algorithms, including six standalone models (M5 prime (M5P), random forest (RF), alternating model trees (AMT), reduced error pruning tree (REPT), locally weighted learning (LWL), and support vector regression (SVR)) were assessed along with six types of hybrid models trained with a bagging (BA) algorithm (BA-M5P, BA-RF, BA-AMT, BA-REPT, BA-LWL, and BA- SVR). A comprehensive database was constructed from 200 eligible people. The models employed post-exercise training WBCs counts as the output parameter and seven WBCs-influencing factors, including intensity and duration of exercise, pre-exercise training WBCs counts, age, body fat percentage, maximal aerobic capacity, and muscle mass as input parameters. Comparing the prediction results of the models to the observed WBCs using standard statistics indicated that the BA-M5P model had the greatest potential to produce a robust prediction of the number of lymphocytes, neutrophils, monocytes, and WBC compared to other models. Moreover, pre-exercise training WBCs counts, intensity and duration of exercise and body fat percentage were the most important features in predicting WBCs counts. These findings hold significant implications for the advancement of exercise immunology and the promotion of public health.

A hybrid intelligent optimization algorithm to select discriminative genes from large-scale medical data

A hybrid intelligent optimization algorithm to select discriminative genes from large-scale medical data

A Scalable Multi-FPGA Platform for Hybrid Intelligent Optimization Algorithms

The Intelligent Optimization Algorithm (IOA) is widely focused due to its ability to search for approximate solutions to the NP-Hard problem. To enhance applicability to practical scenarios and leverage advantages from diverse intelligent optimization algorithms, the Hybrid Intelligent Optimization Algorithm (H-IOA) is employed. However, IOA typically requires numerous iterations and substantial computing resources, resulting in poor execution efficiency. In complex optimization scenarios, IOA traditionally relies on population partitioning and periodic communication, highlighting the feasibility and necessity of parallelization. To address the challenges above, this paper proposes a general hardware design approach for H-IOA based on multi-FPGA. The approach includes the hardware architecture of multi-FPGA, inter-board communication protocols, population storage strategies, complex hardware functions, and parallelization methodologies, which enhance the computing capabilities of H-IOA. To validate the proposed approach, a case study is conducted, in which an H-IOA integrating genetic algorithm (GA), a simulated annealing algorithm (SA), and a pigeon-inspired optimization algorithm (PIO) are implemented on a multi-FPGA platform. Specifically, the flexible job-shop scheduling problem (FJSP) is employed to verify the potential in industrial applications. Two Xilinx XC6SLX16 FPGA chips are used for hardware implementation, encoded in VHDL, and an AMD Ryzen 7 5800U was used for the software implementation of Python programs (version 3.12.4). The results indicate that hardware implementation is 13.4 times faster than software, which illustrates that the proposed approach effectively improves the execution performance of H-IOA.

A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy

To overcome the limitations of single-type intelligent optimization algorithms prone to becoming stuck in local optima for complex problems, a hybrid intelligent optimization algorithm named SDIQ is proposed. This algorithm combines simulated annealing (SA), differential evolution (DE), quantum-behaved particle swarm optimization (QPSO), and improved particle swarm optimization (IPSO) into a unified framework. Initially, SA is used to explore the solution space and guide individuals toward preliminary optimization. The individuals are then ranked by fitness and divided into three subpopulations, each optimized by DE, QPSO, and IPSO, respectively. After each iteration, probabilistic learning based on fitness logarithms facilitates mutual learning among subpopulations, enabling global information sharing and improvement. The experimental results demonstrate that SDIQ exhibits strong global search capability and stability in solving both standard test functions and real-world engineering problems. Compared to traditional algorithms, SDIQ enhances global convergence and solution efficiency by integrating multiple optimization strategies and leveraging inter-individual learning, providing an effective solution for complex optimization problems.

Enhancing mix proportion design of low carbon concrete for shield segment using a combination of Bayesian optimization-NGBoost and NSGA-III algorithm

The demand for segment concrete increases rapidly with the expansion of urban rail transit and underground space, which may lead to carbon emissions (CE). In the production of segment concrete, using supplement cementitious materials (SCM) can be used to reduce CE instead of cement. However, SCM contents are usually determined by many orthogonal experiments, which are often time-consuming and only consider the limited experimental variables. Therefore, a novel hybrid intelligent algorithm combining Bayesian optimization (BO), natural gradient boosting (NGBoost) and non-dominated sorting genetic algorithm (NSGA)-III is proposed, which overcomes these disadvantages. Firstly, NGBoost model after hyperparameter optimization using BO is used to establish a nonlinear mapping relationship between segment concrete mix proportion and performance, which was used as the fitness function of the non-dominated sorting genetic algorithm (NSGA-III). Secondly, the optimal Pareto solutions were obtained through NSGA-III, and the reasonable range of segment concrete mixing proportion was obtained. Finally, a segment concrete production case was used to verify the effectiveness of the proposed algorithm. The results showed that the proposed hybrid algorithm combining proposed scheme decision strategy can obtain an optimal design scheme for concrete mix proportion. In addition, the comprehensive optimization rate of the obtained optimal scheme compared to the experimental optimal group was 11.5%. Moreover, the amount of phosphorous slag (PS) and granulated blast furnace slag (GBFS) in the optimal scheme was 26%, with a mass ratio of 1:2. Compared to the experimental optimal group, the optimal scheme can reduce the cost and CE per cubic meter by 31.64 yuan and 31.04 kg, respectively. Furthermore, the hybrid algorithm proposed can accurately obtains an optimal amount of SCM, which could be used as a supporting tool to reduce the production cost and CE of segment concrete. Overall, this research contributes to combining machine learning algorithms with actual production processes, which overcomes the data limitations of traditional experiments. It also provides a new intelligent approach and reference cases for the low-carbon design and sustainable development of building materials such as concrete.

An uncertain bi-objective mean-entropy model for portfolio selection with realistic factors

In the imprecise investment environment, there are many indeterminate factors impacting security returns. This paper introduces a portfolio optimization problem where cross-entropy is utilized to control portfolio risk within the framework of uncertainty theory and presents an uncertain bi-objective mean-entropy portfolio selection model. To be more realistic, some realistic factors such as minimum transaction lots, dividend factors and tax factors are also considered. By introducing a risk preference coefficient, the bi-objective model is converted into a single-objective model and some equivalents are discussed. Additionally, a hybrid intelligent algorithm integrating a genetic algorithm with uncertain estimation is designed to solve the proposed model. Finally, a case study is executed to confirm the practicability of the model and the performance of the algorithm, and an empirical analysis based on the proposed model and the uncertain mean–variance model is developed to illustrate the advantage of the uncertain mean-entropy model in practical investment.

Hybrid intelligent algorithm and CAD secondary development technology in parameter optimization design of transmission gear set

Hybrid intelligent algorithm and CAD secondary development technology in parameter optimization design of transmission gear set

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Prioritizing the development of public transport is an effective way to improve the sustainability of the transport system. In recent years, bus passenger flow has been declining in many cities. How to reform the operating mode of the public transportation system is an important issue that needs to be solved. An autonomous modular bus (AMB) is capable of physical coupling and uncoupling to flexibly adjust vehicle capacity as well as provide high-quality service under unbalanced passenger demand conditions. To promote AMB adoption and reduce the operating cost of the bus route, this paper presents a joint optimization method to simultaneously determine the AMB dispatching plan, charging plan, and charging infrastructure configuration scheme. Then, a mixed-integer programming model is formulated to minimize the operating costs of the bus route. A hybrid intelligent algorithm combining enumeration, cloning algorithm, and particle swarm optimization algorithm is designed to resolve the formulated model. Subsequently, an actual bus route is taken as an example to validate the proposed method. Results indicate that the developed method in this paper can reduce the operating costs and operational energy consumption of the route compared with the real route operating plan. Specifically, the reduction ratio of the former is 23.85%, and the reduction ratio of the latter is 5.92%. The results of this study validate the feasibility and advantages of autonomous modular transit service, contributing positively to the sustainable development of the urban public transportation system.

Fault diagnosis in high-speed computing systems using big data analytics integrated evolutionary computing on the Internet of Everything platform

The significance of high-speed computing systems is paramount for diverse applications. However, their intricate structure and rigorous operational prerequisites render them susceptible to malfunctions. The conventional fault diagnosis models need help managing the copious data produced by these systems, resulting in diagnostic procedures that are both time-consuming and ineffective. The research proposed an Internet of Everything (IoE) platform and big data analytics (BDA) based Fault Diagnosis System (IBFDS) in conjunction with evolutionary computing techniques. The system integrates a hybrid intelligent algorithm that amalgamates the advantages of evolutionary computing and machine learning to enhance the precision and effectiveness of fault diagnosis. The utilization of big data analysis methodologies facilitates the processing and examination of vast quantities of system data, thereby enabling the comprehensive detection and identification of faults. Furthermore, cloud services based on the IoE allow data storage, administration, and instantaneous cooperation among various parties involved.
 Furthermore, a detection technique utilizing a Reduced Boltzmann Machine (RBoM) improves the precision of fault diagnosis. The efficacy of the proposed IBFDS was demonstrated through experimental evaluation using a high-speed computing system dataset. The results obtained were impressive, with a fault detection rate (94.11%), fault identification accuracy (93.76%), fault localization accuracy (93.71%), false positive rate (1.54%), and false negative rate (0.98%). The findings of this study make a valuable contribution to the advancement of fault diagnosis systems that are resilient and effective for high-speed computing systems. This facilitates proactive maintenance, enhances system reliability, and optimizes system performance.

Reliability analysis of tunnels considering the rock-support interaction using a metamodel-based reliability method

The convergence-confinement method (CCM) is an important approach to analysis the rock-support interaction and to design tunnel and its support. To make an effort on efficient reliability analysis of tunnels integrated with CCM, of which performance functions are mostly highly nonlinear and implicit, a hybrid intelligent algorithm combing uniform design (UD) and Gaussian process regression (GPR) is proposed. Based on the generated UD samples, GPR-parameter metamodels to predict the important parameters of CCM and GPR-probability metamodels to approximate the real performance functions are constructed consequently. Then sensitivity analysis, determination of the optimal preliminary support timing and failure probabilities calculations can be performed in conjunction with Monte Carlo simulation (MCS) based on the constructed GPR-parameter metamodels and GPR-probability metamodels. To verify the efficiency and accuracy of the proposed method, a typical circle tunnel is analyzed and good results are achieved. Finally, a practical hydraulic tunnel is analyzed using the proposed method and some beneficial conclusions are drawn.

Multi-objective disassembly line design and optimisation considering energy efficiency and human factors

Green manufacturing and remanufacturing revolve around recycling end-of-life (EOL) products. One crucial aspect of this process is the disassembly line balancing problem (DLBP), which involves assigning disassembly tasks to workstations in a logical order. However, current DLBP models often overlook operator-related factors and energy efficiency. This paper introduces a multi-objective DLBP model that considers both operator and energy factors. The model aims to minimise disassembly line idle rate, improve line smoothness, reduce energy consumption, and balance operator workload under multiple constraints. Since this problem is NP-hard, we also develop a hybrid intelligent algorithm that combines simulated annealing and the water cycle algorithm (SWCA) to solve DLBP effectively using real-world case disassembly. For multi-objective optimisation problems, choosing multiple non-inferior solutions can be challenging. In this work, we embrace an ‘optimisation before decision-making' approach, integrating multi-criteria decision-making techniques with neural networks optimised by a particle swarm optimisation (PSO) algorithm to identify the most satisfactory solution.

Intelligent Task Allocation and Planning for Unmanned Surface Vehicle (USV) Using Self-Attention Mechanism and Locking Sweeping Method

The development of intelligent task allocation and path planning algorithms for unmanned surface vehicles (USVs) is gaining significant interest, particularly in supporting complex ocean operations. This paper proposes an intelligent hybrid algorithm that combines task allocation and path planning to improve mission efficiency. The algorithm introduces a novel approach based on a self-attention mechanism (SAM) for intelligent task allocation. The key contribution lies in the integration of an adaptive distance field, created using the locking sweeping method (LSM), into the SAM. This integration enables the algorithm to determine the minimum practical sailing distance in obstacle-filled environments. The algorithm efficiently generates task execution sequences in cluttered maritime environments with numerous obstacles. By incorporating a safety parameter, the enhanced SAM algorithm adapts the dimensional influence of obstacles and generates paths that ensure the safety of the USV. The algorithms have been thoroughly evaluated and validated through extensive computer-based simulations, demonstrating their effectiveness in both simulated and practical maritime environments. The results of the simulations verify the algorithm’s capability to optimize task allocation and path planning, leading to improved performance in complex and obstacle-laden scenarios.

Construction of an intelligent optimization model for water resource allocation under drought conditions

Abstract In this paper, we first establish the model of optimal allocation of water resources within the region and convert it into a dynamic planning problem. Then, under drought conditions, the models of optimal allocation of intra-regional water resources among industries and different crops are established, respectively, and the water production function model is established using sensitivity coefficients. Under the condition of satisfying the water quantity opportunity constraints and with the objective of ensuring obtaining a higher system return, the water resources optimal allocation model based on interval multi-stage stochastic opportunity constraint planning is established by combining multi-stage planning, interval planning and stochastic opportunity constraint planning. A hybrid intelligent algorithm combining stochastic simulation, multilayer neural networks, and genetic algorithms is used to solve the model. A case study of optimal water resource allocation is used to verify the feasibility and validity of the proposed model and algorithm. Under the traditional water resource allocation scheme, the total water supply and water shortage of Nandan County are 0.97 and 0.58 billion m3, and the water shortage rate is 37.55%. Under the optimized water resources allocation scheme of this paper, the water shortage of Nandan County is 0.05 billion m³, and the water shortage rate is 6.09%. The model of this paper’s allocation scheme can effectively meet the water demand.