Strong Search Research Articles

Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multi-receptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis.

Screening of Drought-Resistant Potato Germplasm Resources Based on Intelligent Algorithm

As a staple food resource, potato is of great significance for improving grain reserves and ensuring national food security. In order to improve potato yield and promote the process of potato becoming a staple food, a K-means algorithm optimized by particle swarm algorithm was proposed to realize the screening of dry potato germplasm resources. First, the research continues the research on particle swarm optimization, and innovatively applies K-means algorithm to optimization. The research utilizes the advantages of particle swarm optimization, such as fast convergence speed, strong search ability, and simple operation, to enable particle swarm optimization to take on the role of optimizing the initial clustering center, thereby improving the accuracy and efficiency of clustering analysis. On this basis, a PSO-K-means drought resistant potato germplasm resource screening model was constructed. This model consists of a data collection and preprocessing module, an impact indicator determination module, and a comprehensive evaluation module. Finally, the application effect of the model was verified. The results show that the AUC value of the model is up to 0.840, and the screening accuracy is as high as 94.5%, which is 13.5% higher than that of the K-means model. The research method has been validated to improve the limitations of K-means mode, such as high screening error, weak stability, and falling into local optimal solutions. It optimizes the screening effect of drought resistant potato germplasm resources, which is conducive to exploring the potential of potato resources. In addition, research has also provided broader ideas for the optimization and application of particle swarm optimization algorithms.

Multi-objective reinforcement learning based on nonlinear scalarization and long-short-term optimization

PurposeMany practical control problems require achieving multiple objectives, and these objectives often conflict with each other. The existing multi-objective evolutionary reinforcement learning algorithms cannot achieve good search results when solving such problems. It is necessary to design a new multi-objective evolutionary reinforcement learning algorithm with a stronger searchability.Design/methodology/approachThe multi-objective reinforcement learning algorithm proposed in this paper is based on the evolutionary computation framework. In each generation, this study uses the long-short-term selection method to select parent policies. The long-term selection is based on the improvement of policy along the predefined optimization direction in the previous generation. The short-term selection uses a prediction model to predict the optimization direction that may have the greatest improvement on overall population performance. In the evolutionary stage, the penalty-based nonlinear scalarization method is used to scalarize the multi-dimensional advantage functions, and the nonlinear multi-objective policy gradient is designed to optimize the parent policies along the predefined directions.FindingsThe penalty-based nonlinear scalarization method can force policies to improve along the predefined optimization directions. The long-short-term optimization method can alleviate the exploration-exploitation problem, enabling the algorithm to explore unknown regions while ensuring that potential policies are fully optimized. The combination of these designs can effectively improve the performance of the final population.Originality/valueA multi-objective evolutionary reinforcement learning algorithm with stronger searchability has been proposed. This algorithm can find a Pareto policy set with better convergence, diversity and density.

CHEABC-QCRP: A novel QoS-aware cluster routing protocol for industrial IoT

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN.

Optimization design of brushless DC motor based on improved JAYA algorithm

Brushless direct current motor is widely used in industrial production because of its simple structure, wide speed range and low noise. To improve the operation efficiency of brushless DC motor and reduce the production and application costs, the optimization of brushless DC motor is analyzed by introducing the JAYA algorithm. This method determines the optimal parameters of a brushless DC motor using the theory of electromagnetic structure parameter selection and efficiency calculation. The population diversity of the JAYA algorithm is improved through an empirical learning strategy, and an adaptive strategy is introduced to balance the development ability and search performance of the algorithm. This ensures population diversity and improves convergence speed. The experiment showcases that the improved JAYA algorithm has a lower rank average in unimodal function operations, demonstrating stronger local development ability and better stability. It exhibits strong search ability in many local optima of multimodal functions. Moreover, the motor's average efficiency after optimization is 94.48%. The algorithm reaches the global optimum after approximately 40 iterations and offers faster convergence speed and higher accuracy. The adaptive JAYA algorithm is stable at around 93% when the number of iterations reaches 90, with a maximum efficiency of 95.3%. It is 5–12 percentage points higher than the other three comparison algorithms. The optimal solution of the motor parameters in the adaptive JAYA algorithm is closest to the theoretical parameter optimization value, meeting both the constraints of variables and the constraints of the model. The stator diameter, tooth magnetic induction, winding current density, air gap magnetic induction, and stator yoke magnetic induction values are 201.5 mm, 1.8 T, 2.049 A/mm2, 0.63 T, and 0.91 T, respectively. The research overcomes the problem of parameter optimization in the optimization design of brushless DC motor, improving their economic value of brushless DC motor in industrial production and application.

Planar weibull quantum circuit genetic algorithm with strong search ability and its implementation

Genetic Algorithm (GA) is a widely used search method, and Quantum Genetic Algorithm (QGA) has been pointed out for several years. While common QGA only refers to the concept of quantum mechanics, and still uses mathematical representations of quantum states to simulate quantum entanglement and superposition, it can’t be realized on a quantum computer. This paper presents an implementable quantum circuit for a genetic algorithm, named as Planar-Weibull Quantum Circuit Genetic Algorithm (PW-QCGA), employing the Weibull function and excitation function through planar matrix coding. It is not only a self-adaptive algorithm that adjusts parameters dynamically, but it can also be executed on quantum computers by constructing parameterized quantum circuits. Experiments on three typical applications show that PW-QCGA not only has obvious advantages on convergence accuracy, search ability, convergence speed, and robustness, but also has higher fidelity on a quantum computer. We hope this work can bring inspiration to other quantum intelligent algorithms.

Modeling and structural optimization design of switched reluctance motor based on fusing attention mechanism and CNN-BiLSTM

Six-degrees of freedom (6-DOF) parallel mechanisms driven by switched reluctance motors (SRMs) can realize flexible control with high precision. Efficiency is an important indicator to measure the speed control system of SRMs. There are many characteristic factors affecting efficiency and strong nonlinear relationships between different characteristic parameters, which makes it difficult for analytical models and traditional neural network models to express their spatial correlation. For this reason, a convolutional neural network (CNN)-bidirectional long short-term memory network (BiLSTM) efficiency regression prediction model (CNN-BiLSTM-SENet) that integrates the attention mechanism (SENet) is proposed. Firstly, customize a formula for sensitivity analysis to screen characteristic parameters of efficiency. Secondly, build the CNN-BiLSTM-SENet model and use sparrow search algorithm (SSA) for hyperparameter optimization, input data to CNN to extract high-dimensional feature vectors that reflect complex changing relationships between features and efficiency while establishing feature channels. Embed the SENet to adaptively perceive and assign different weights to the feature channels, enhancing the influence of key features. Input the feature vectors outputted by the front-end network to BiLSTM to bidirectionally learn coupling relationships between sequences and complete regression prediction. Finally, propose improved northern goshawk optimization (MNGO) to solve the regression model to obtain the maximum efficiency and corresponding characteristic parameters. The results proved that the SSA-optimized CNN-BiLSTM-SENet model has higher fitting exactness and better prediction effect for efficiency regression prediction, and the MNGO also has stronger search ability and faster convergence.

Separation of Temperature-Induced Response for Bridge Long-Term Monitoring Data Using Local Outlier Correction and Savitzky–Golay Convolution Smoothing

This study proposed a separation method to identify the temperature-induced response from the long-term monitoring data with noise and other action-induced effects. In the proposed method, the original measured data are transformed using the local outlier factor (LOF), and the threshold of the LOF is determined by minimizing the variance of the modified data. The Savitzky-Golay convolution smoothing is also utilized to filter the noise of the modified data. Furthermore, this study proposes an optimization algorithm, namely the AOHHO, which hybridizes the Aquila Optimizer (AO) and the Harris Hawks Optimization (HHO) to identify the optimal value of the threshold of the LOF. The AOHHO employs the exploration ability of the AO and the exploitation ability of the HHO. Four benchmark functions illustrate that the proposed AOHHO owns a stronger search ability than the other four metaheuristic algorithms. A numerical example and in situ measured data are utilized to evaluate the performances of the proposed separation method. The results show that the separation accuracy of the proposed method is better than the wavelet-based method and is based on machine learning methods in different time windows. The maximum separation errors of the two methods are about 2.2 times and 5.1 times that of the proposed method, respectively.

Multiple individual guided differential evolution with time varying and feedback information-based control parameters

Differential evolution (DE) is a simple and efficient metaheuristic algorithm for solving global optimization problems. It is used widely in various fields due to its concise structure and strong search ability. In this study, an extended version of the DE named multiple individual guided differential evolution (MGDE) is proposed. The MGDE is distinguished by introducing a novel mutation strategy based on multiple guiding individuals of the DE population to manage the diversity and convergence. The base vector of the mutation strategy is defined as a center of guiding individuals and the difference vectors are assigned to perform a search towards one of the top fitted and top diversified individuals available in the population. The control parameters of the DE are adjusted in a way to provide a suitable transition from exploration to exploitation and to utilize the information of recent success history of evolution. The performance of the proposed MGDE is evaluated on three different benchmark sets including IEEE CEC2014, IEEE CEC2017, and IEEE CEC2011 of real-world problems. Different performance metrics such as average and standard deviation of fitness, ranking of algorithms, Wilcoxon signed-rank test, and convergence analysis are used to analyze and compare the results of the MGDE with several other metaheuristic algorithms. Comparison attest that the proposed MGDE algorithm is highly competitive with the other metaheuristic algorithms.

Improving the tensile strength of non-keyhole friction stir lap welding joint of 2024-T4 Al alloy by radial basis function neural network and improved particle swarm optimization algorithm

The non-keyhole friction stir lap welding (N-KFSLW) technology assisted by the outer stationary shoulder and the inner upper half-thread rotating pin was proposed to obtain the welding joint without keyhole through one-time process. Choosing 2024 aluminum alloys as the research object, the formation, microhardness and tensile strength of N-KFSLW joint were investigated. The improved particle swarm optimization (IPSO) algorithm was newly developed and had the advantages of large convergence speed and strong search ability, by which the radial basis function (RBF) neural network was optimized to enhance its prediction accuracy. After that, the RBF and IPSO (IPSO-RBF) system was used to predict the joint strength and optimize the process parameters combination. The results showed that the lap joint had not only the SZ with the thickness almost equal to the thickness of upper sheet but also the cold lap with a very small height, thereby leading to the high tensile strength of joint. The optimized parameters of welding speed, rotating speed and pin type by the IPSO-RBF system were respectively 612 rpm, 80 mm/min, and upper half-thread pin, and the tensile strength of lap joint reached 11.88 kN/mm. The N-KFSLW technology assisted by upper half-thread pin provides an effective way to obtain the lap joint with high performance, and the IPSO-RBF system can be used to maximize the strength of welding joint.

Multi-objective particle swarm optimization algorithm using Cauchy mutation and improved crowding distance

PurposeMulti-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.Design/methodology/approachIn this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.FindingsIn order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.Originality/valueIn order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

An improved gravitational search algorithm combining with centripetal force

Although GSA has strong search ability, it is still easy to fall into local optimum and accuracy is often not high enough. Inspired by the concept of centripetal force, we propose a centripetal force based gravity search algorithm, named CF-GSA. It controls the parameters in different stages, which balances the exploration and exploitation capabilities of particles. We performed comparative experiments on 23 benchmark functions, and analyzed the performance of this algorithm. In experiments, it can effectively prevent falling into local optimun and achieve higher accuracy and better convergence.

Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.

A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism

Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set (RefSet), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet, and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems.

An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning

A global path planning method based on improved particle swarm optimization (PSO) algorithm was proposed to find a high quality flight trajectory in three-dimensional complex environment under multiple threats for UAV. The improved path planning algorithm combines the standard PSO with A* method to compensate for the slow convergence rate of PSO. The objective function with multiple constraints of A* method is used to evaluate the quality of the waypoint, and the objective function of PSO algorithm is designed to evaluate the quality of the candidate path. To verify the effectiveness of the improved algorithm, the improved PSO algorithm is used to compare with basic PSO algorithms. The experiment in complex environment shows it has stronger search ability, convergence ability due to the improvement of population diversity and convergence speed.

Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm

Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost Algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector Machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO); through the real-time update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method forms a new gas group cooperation. The improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China show that the diagnosis method proposed in this paper has strong search ability and fast convergence speed and has a significant improvement in diagnostic accuracy compared with traditional methods.

Parameter identification of ship motion mathematical model based on full-scale trial data

Using the full-scale trial data of vessel YUKUN, a parameter identification scheme, Support Vector Regression (SVR) combined with modified grey wolf optimizer (MGWO), is proposed for identifying ship motion model. This study establishes response mathematical model and 4 Degrees of Freedom (DOF) nonlinear whole-ship mathematical model of vessel YUKUN. The full-scale trial data of vessel YUKUN are processed and analyzed. In the study of grey box identification modeling, the rough parameters reference values of ship response mathematical model and nonlinear whole-ship mathematical model are obtained by SVR. Based on these rough reference values, the MGWO algorithm and Firefly Algorithm are used to further optimize them to obtain final parameter identification values. The final parameter identification values obtained by the three algorithms and recursive least squares with forgetting factor are substituted into the mathematical model to obtain the prediction results of ship motion state. The final prediction results of ship motion state show that the MGWO algorithm has strong search and optimization ability. In the case that the reference values obtained by SVR are not accurate, MGWO algorithm can be used to find accurate parameters of ship motion mathematical model in a given range. The proposed scheme provides a reference for the parameter identification of ship motion mathematical model.

Artificial bee colony based on adaptive search strategy and random grouping mechanism

As a popular global optimization algorithm, artificial bee colony (ABC) has strong search ability and simple concept. However, ABC has some deficiencies. The exploitation ability of ABC is not as strong as its exploration ability. The original roulette selection in the onlooker bee search will gradually lose its effect with increasing of iterations. In order to tackle the above problems, an efficient ABC based on adaptive search strategy and random grouping mechanism (called ASRGABC) is proposed in this paper. Firstly, an adaptive search strategy is designed by comparing the success rate of the current and previous iterations. According to the changes of the success rate, a suitable search strategy is adaptively selected. Then, a random grouping mechanism is proposed to replace the original roulette selection. The whole population is randomly divided into several groups. The onlooker bees are allowed to follow the best solution in each group. Based on the random grouping, the search strategy is modified. Thirdly, opposition-based learning is employed to enhance the scout bee phase. To verify the performance of ASRGABC, 22 classical benchmark problems and 28 CEC 2013 benchmark problems are tested. Experimental results show ASRGABC obtains better performance than thirteen other ABC variants.

Dynamic opposite learning enhanced dragonfly algorithm for solving large-scale flexible job shop scheduling problem

Flexible job shop scheduling problem (FJSP) has attracted many research interests, in particular for meta-heuristic algorithm (MA) developers due to the superior optimization performance. Dragonfly algorithm (DA) is one of recent and popular MA approaches. However, it is inevitable for DA to be trapped into local optima, especially when dealing with the complex large-scale flexible job shop scheduling problem (LSFJSP). In this paper, an improved DA, adopting a dynamic opposite learning (DOL) strategy, is proposed (namely DOLDA) to solve the LSFJSP. DOL strategy is embedded into the population initialization stage and the generation jumping stage to raise the search ability of DA. This paper uses a T-test to testify whether there are differences between the proposed algorithm and other comparison algorithms, comparison results indicate that DOLDA shows noticeable differences with other algorithms that explain the effectiveness and innovation of the proposed algorithm. The jump rate is an important parameter that determines the probability of algorithms escaping from the algorithms’ local optimal solution. This paper also considers the jump rate analysis experiments to maximize the power of the DOLDA. 28 test functions from CEC 2013 and CEC 2014 are applied to verify the performance of DOLDA, test results reveal that DOLDA owns strong search ability in coping with almost all test functions. The DOLDA also is applied to solve 15 LSFJSP instances generated by Brandimate rule, the results obtained show that DOLDA can efficiently achieve a better solution on the LSFJSP compare to compared algorithms.

An enhanced black widow optimization algorithm for feature selection

Feature selection is an important data processing method to reduce dimension of the raw datasets while preserving the information as much as possible. In this paper, an enhanced version of Black Widow Optimization Algorithm called SDABWO is proposed to solve the feature selection problem. The Black Widow Optimization Algorithm (BWO) is a new population-based meta-heuristic algorithm inspired by the evolution process of spider population. Three main improvements were included into the BWO to overcome the shortcoming of low accuracy, slow convergence speed and being easy to fall into local optima. Firstly, a novel strategy for selecting spouses by calculating the weight of female spiders and the distance between spiders is proposed. By applying the strategy to the original algorithm, it has faster convergence speed and higher accuracy. The second improvement includes the use of mutation operator of differential evolution at mutation phase of BWO which helps the algorithm escape from the local optima. And then, three key parameters are set to adjust adaptively with the increase of iteration times. To confirm and validate the performance of the improved BWO, other 10 algorithms are used to compared with the SDABWO on 25 benchmark functions. The results show that the proposed algorithm enhances the exploitation ability, improves the convergence speed and is more stable when solving optimization problems. Furthermore, the proposed SDABWO algorithm is employed for feature selection. Twelve standard datasets from UCI repository prove that SDABWO-based method has stronger search ability in the search space of feature selection than the other five popular feature selection methods. These results confirm the capability of the proposed method simultaneously improve the classification accuracy while reducing the dimensions of the original datasets. Therefore, SDABWO-based method was found to be one of the most promising for feature selection problem over other approaches that are currently used in the literature.