Combining Particle Swarm Optimization Algorithm Research Articles

High-κ monocrystalline dielectrics for low-power two-dimensional electronics.

The downscaling of complementary metal-oxide-semiconductor technology has produced breakthroughs in electronics, but more extreme scaling has hit a wall of device performance degradation. One key challenge is the development of insulators with high dielectric constant, wide bandgap and high tunnel masses. Here, we show that two-dimensional monocrystalline gadolinium pentoxide, which is devised through combining particle swarm optimization algorithm and theoretical calculations and synthesized via van der Waals epitaxy, could exhibit a high dielectric constant of ~25.5 and a wide bandgap simultaneously. A desirable equivalent oxide thickness down to 1 nm with an ultralow leakage current of ~10-4 A cm-2 even at 5 MV cm-1 is achieved. The molybdenum disulfide transistors gated by gadolinium pentoxide exhibit high on/off ratios over 108 and near-Boltzmann-limit subthreshold swing at an operation voltage of 0.5 V. We also constructed inverter circuits with high gain and nanowatt power consumption. This reliable approach to integrating ultrathin monocrystalline insulators paves the way to future nanoelectronics.

Computational design of residential units’ floor layout: A heuristic algorithm

Computer-aided intelligent design can improve the quality and efficiency of residential building design. However, the design conditions are diverse, and numerous combinations of spatial elements exist. We propose a heuristic algorithm—the combined Monte Carlo tree search (MCTS) and particle swarm optimization (PSO) algorithm (PSO-MCTS)—for the automated design of residential floor layouts. The MCTS algorithm deals with discrete variables (room locations). It considers architectural design experience and design standards during node pruning to compress the search space and simplify the computation. The PSO algorithm deals with continuous variables (room sizes) and accelerates the computing speed due to parallel processing. The PSO-MCTS algorithm achieves a good balance between computational efficiency and accuracy. It is suitable for various design conditions and requirements. The input parameters include the daylight façade, boundary scope, entrance location, room type and size, and relationships with adjacent rooms. The PSO-MCTS provides 100 %, 80 %, and 75 % better results than heuristic algorithms, designs by architects, and a state-of-the-art deep learning model, respectively.

Research on proportional valve spool wear diagnostic method hybrid-driven by the valve port energy loss mechanism model and data

High performance proportional valves are commonly utilized for precise control of aircraft actuators to ensure flight safety. Fault diagnosis play a crucial role in maintaining equipment reliability. However, traditional diagnostic methods using vibration signals face challenges such as inability to directly measure failure points, fault characteristics being influenced by sensor position, and large data processing volume, limiting engineering application scope. This paper introduces a valve spool wear fault diagnostic method based on energy loss model and data hybrid drive, leveraging throttling loss characteristics. An energy loss failure mechanism model, incorporating differential pressure and flow rate of valve port, was established, and experimentally verified. This method effectively addresses the limitations of vibration sensors in diagnosing aviation hydraulic systems. By combining particle swarm optimization algorithm with deep extreme learning machine, the number of neurons, weight distribution, and data set ratio are rapidly optimized, reducing data processing complexity and enhancing diagnostic efficiency. Comparative tests demonstrate a significant increase in average diagnostic accuracy rate, with more than 8% improvement after integrating energy loss information and particle swarm optimization, reaching over 98%. The proposed method exhibits superior performance in average diagnostic accuracy rate and stability compared to other methods and can be served as a valuable reference for predicting faults in aviation hydraulic valves.

Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the basis for this verification. Once the existence of the REV for such sediment was confirmed, we established a machine-learning predictive model. This model utilizes a hybrid algorithm combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM). The model was trained using a database generated from the aforementioned physical tests. The machine-learning model demonstrates favorable predictive performance based on several statistical metrics, including the coefficient of determination (R2), mean residual error (MSE), mean relative residual error (MRSE), and the correlation coefficient R during the verification process. Utilizing the established machine-learning predictive model, one can effortlessly obtain the permeability tensor of marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure by inputting the relevant stress condition parameters. The original research cannot estimate the permeability tensor under similar loading and unloading conditions through REV. In this study, the physical model test was used to determine the REV of marine cohesive sediments with cracks by cyclic-constrained pressure loading and unloading. Referring to the results of physical tests, we developed a machine-learning prediction model that can easily estimate the permeability tensor of marine cohesive sediments with cracks under cyclic loading and constrained pressure unloading conditions. This method greatly saves time and computation and provides a direct method for engineering and technical personnel to predict the permeability tensor in this case.

Collaborative Task Allocation and Optimization Solution for Unmanned Aerial Vehicles in Search and Rescue

Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.

Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.

Integrated Brain Tumor Detection: PSO-Guided Segmentation with U-Net and CNN Classification

Segmentation of tissues in magnetic resonance (MR) images is vital for effective analysis of brain pathology. However, existing approaches for brain MR image segmentation and feature extraction have limitations such as high computational costs and lower accuracy. To overcome these challenges, this paper presents an integrated approach, termed PSO-Guided Segmentation with U-Net and CNN Classification, for the detection and quantification of brain pathology in MR images. The proposed algorithm combines Particle Swarm Optimization (PSO) for enhanced feature extraction, for the classification CNN while segmentation can be done through U-Net. Numerous tests show that the suggested algorithm performs more effectively and accurately than existing techniques in terms of sensitivity and accuracy. By utilizing cutting-edge technologies like Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO), and U-Net, significant progress has been made in enhancing the accuracy of MR image segmentation and tumor detection. Compared to traditional methods, our proposed methodology outperforms, enhancing accuracy and sensitivity by approximately 0.44%, significantly decreasing computational expenses while maintaining high precision. However, due to the dynamic nature of technology advancements, it remains imperative to continually explore and develop novel frameworks and algorithms to further improve accuracy and achieve the highest level of performance.

Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project.

Research on Assembly Sequence Optimization Classification Method of Remanufacturing Parts Based on Different Precision Levels

Aiming at resolving the problem of low assembly accuracy and the difficulty of guaranteeing assembly quality of remanufactured parts, an optimization classification method for the assembly sequence of remanufactured parts based on different accuracy levels is proposed. By studying the characteristics of recycled parts, based on the requirement that the quality of remanufactured products not be lower than that of the assembly quality of new products, the classification selection matching constraints of remanufactured parts are determined, and the classification selection matching optimization models of remanufactured parts with different precision levels is established. An algorithm combining particle swarm optimization and a genetic algorithm is proposed to solve the model and obtain the optimal assembly sequence. Taking the remanufacturing assembling of a 1.4 TGDI engine crank and a connecting rod mechanism as an example, the comparison of quality data shows that this method can effectively improve the qualified rate of assembly, reduce the cost of after-sale claims, provide new theories and methods for remanufacturing enterprises that need hierarchical assembly, and provide effective guidance for the development of the remanufacturing industry.

Improved parameter optimization method for measurement device independent protocol

The optimal selection of parameters in practical quantum key distribution can greatly improve the key generation rate and maximum transmission distance of the system. Owing to the high cost of global search algorithm, local search algorithm is widely used. However, there are two shortcomings in local search algorithm. One is that the solution obtained is not always the global optimal solution, and the other is that the effectiveness of the algorithm is greatly dependent on the choice of initial value. This paper uses the Monte Carlo method to prove whether the key generation rate function is convex, and also simulates and analyzes the projection of the key generation rate function on each dimension of the parameter, in contrast to the approach in previous article. In order to eliminate the effect of the initial value, this paper proposes the particle swarm local search optimization algorithm which integrates particle swarm optimization algorithm and local search algorithm. The first step is to use the particle swarm optimization to find a valid parameter which leads to nonzero key generation rate, and the second step is to adopt the parameter as the initial value of local search algorithm to derive the global optimal solution. Then, the two algorithms are used to conduct simulation and their results are compared. The results show that the key generation rate function is non-convex because it does not satisfy the definition of a convex function, however, since the key generation rate function has only one non-zero stagnation point, the LSA algorithm can still obtain the global optimal solution with an appropriate initial value. When the transmission distance is relatively long, the local search algorithm is invalid because it is difficult to obtain an effective initial value by random value method. The particle swarm optimization algorithm can overcome this shortcoming and improve the maximum transmission distance of the system at the cost of slightly increasing the complexity of the algorithm.

Credibility-based chance-constrained multimode resource-constrained project scheduling problem under fuzzy uncertainty

This paper investigates a multimode resource-constrained project scheduling problem under hybrid uncertain environment. With the activity durations are fuzzy variables, the resource requests change with the durations. The goal is to minimize the project duration and the cost associated with the required resources. Considering the stochastic duration and resource demand, a chance constrained programming model based on credibility is established. In order to solve the model, the chance constraints and fuzziness are treated by introducing the credibility distribution function and its inverse function. And, a hybrid algorithm combining particle swarm optimization (PSO) and genetic algorithm (GA) is proposed. It has shown fine performance in solving problems with 30, 60, and 120 activities. We also analyze the sensitivity of confidence, fuzziness, number of activities, number of modes, and resource unit cost. Meanwhile, fuzzy value-at-risk is used as a risk measure, and a model based on it is constructed. These numerical results show that the developed algorithm can obtain the near optimal solution of large-scale projects in a reasonable calculation time.

Hydrodeoxygenation of stearic acid to produce diesel–like hydrocarbons: kinetic modeling, parameter estimation and simulation

• Different assumptions for the adsorption step were tested to develop a suitable kinetic model for the hydrodeoxygenation of stearic acid. • Reaction rates constants, equilibrium adsorption constants and activation energies were estimated. • The model based on non-dissociate H 2 and single-site adsorption provided the best fitting quality. • The Peng-Robinson equation of state had better performance than the Predictive-Soave-Redlich-Kwong equation to calculate hydrogen solubilities in the reaction mixture. There has been an increasing search for renewable sources to produce alternative biofuels due to the growth in the global energy demand and the environmental impacts caused by fossil fuels. Green diesel obtained via hydrotreatment of vegetable oils and fats is a promising option, mainly due to the possibility of being integrated into an existing refinery. Here, we investigate the kinetics of hydrodeoxygenation of stearic acid as a model compound of triglyceride raw materials over NiMo/Al 2 O 3 catalyst to obtain hydrocarbons in the diesel range. Kinetic models based on different assumptions to the adsorption step, such as Eley–Rideal and Langmuir–Hinshelwood mechanisms, were evaluated through nonlinear regression to parameter estimation using a hybrid algorithm combining Particle Swarm Optimization and Nelder–Mead method. All proposed models fitted well to the experimental data, presenting correlation coefficients higher than 90% for the fatty acid and the final hydrocarbons. However, the surface reaction limited model based on competitive adsorption with non-dissociate H 2 adsorbed showed the best results and was used to simulate the process.

Recognition and Detection Methods of Artificial Intelligence in Computer Network Faults under the Background of Big Data

With the widespread use of computers and the rapid development of Internet technology, computer application technology has become more and more important in people’s work and life. The article mainly studies particle swarm optimization (PSO) and radial basis neural network function (RBF). Particle swarm optimization is an evolutionary swarm intelligence algorithm, such as nonderivative node transfer function or gradient information loss. Because its principle is simple and easy to implement, it can deal with some problems that cannot be solved by traditional methods. It is widely used in neural networks, and has achieved good results in many fields such as network training, performance optimization, and system mismanagement. RBF neural network is a feedforward neural network, which overcomes the shortcomings of traditional neural network learning process that the convergence is highly dependent on the initial value and can only be partially converged. This paper organically combines PSO algorithm and RBF neural network to study the detection and detection of computer network faults. The results show that although the prediction error of the improved network model on the experimental test set is still only 89.3% different from that of the SVM model, its convergence time is reduced to 0.90699885 s, which can effectively detect and identify computer network faults.

QoS-Aware Algorithm Based on Task Flow Scheduling in Cloud Computing Environment.

This paper deals with the challenging problem of scheduling users’ tasks, while taking into consideration users’ quality of service (QoS) requirements, with the objective of reducing the energy consumption of physical machines. This paper presents a model to analyze the current state of the running tasks according to the results of the QoS prediction assigned by an ARIMA prediction model optimized with Kalman filter. Then, we calculate a scheduling policy with a combined particle swarm optimization (PSO) and gravitational search algorithm (GSA) algorithms according to the QoS status analysis. Experimental results show that the proposed HPSO algorithm reduces resources consumption 16.51% more than the original hybrid algorithm, and the violation of service-level agreement (SLA) is 0.053% less when the optimized prediction model is used.

Multiobjective Planning Strategy for the Placement of Electric-Vehicle Charging Stations Using Hybrid Optimization Algorithm

Electric Vehicle (EV) charging station placement problem is a facility location problem. The EV charging station placement problem concerns about the total coverage in traffic network, the system losses and node voltage deviations in electric distribution system. To address the loss reduction and voltage profile improvement, the distribution systems are normally equipped with shunt capacitors for reactive power compensation. In this paper mathematical model comprising three objective functions, maximization of coverage and minimization of loss and node voltage deviations subjected to constraints is proposed for the simultaneous placement of EV charging stations and shunt capacitors. The control variables for optimization are the rating and location of charging stations and shunt capacitors. A hybrid optimization algorithm (PSO-DS) combining particle swarm optimization algorithm and direct search method is proposed for the solution of the mathematical model. The performance of PSO-DS is justified by comparing it with other state-of-the-art algorithms in solving the standard benchmark functions. Simulations are carried out on a 33-bus distribution system and a 25-node traffic network system to determine the different planning strategy for the placement of charging stations.

Improving the Resilience of Port–Hinterland Container Logistics Transportation Systems: A Bi-Level Programming Approach

Port–hinterland container logistics transportation systems (PHCLTSs) are significant to economic and social development. However, various kinds of unconventional emergency events (UEEs), such as natural or human-caused disasters, threaten PHCLTSs. This study aims to measure and improve the resilience of PHCLTSs. Bi-level programming models with two different lower level models are established to help PHCLTSs recover their capacity efficiently in the face of UEEs. In the upper level model, the government makes immediate recovery decisions about a damaged PHCLTS with the goal of improving the resilience of the PHCLTS. In the lower level models, truck carriers make decisions about transportation routes and freight volume in the recovered PHCLTS. They cooperate fully to pursue the maximization of total profit and are coordinated by a central authority, or they make their own decisions to pursue maximization of their own profit noncooperatively. An algorithm combining particle swarm optimization (PSO) and traditional optimization algorithms is proposed to solve the bi-level programming models. The numerical experimental results show the validity of the proposed models.

Evaluation of regional industrial cluster innovation capability based on particle swarm clustering algorithm and multi-objective optimization

With the progress of the times and the development of science, industrial clusters have been regarded by all countries in the world as one of the important ways to enhance regional competitiveness, and become an inevitable trend of industrial development. The research on the innovation ability of industrial clusters can not only maintain sustainable development of industrial clusters and obtain sustained competitive advantages, but also provide reference for the government's policy formulation of industrial clusters. This paper aims to study the evaluation of regional industrial clusters' innovation capability based on particle swarm clustering and multi-objective optimization. This paper uses the theory of industrial cluster innovation and takes regional industrial system as the empirical research object to establish a regional industrial system capability evaluation system, which is based on the selection of indicators, combined with analytic hierarchy process and factor analysis to evaluate industrial innovation capability. On this basis, the particle swarm clustering theory is used to verify the innovation ability and evaluation index system of industrial clusters, and provide a reference for the evaluation of the innovation ability of industrial clusters. This paper divides the regional cluster innovation capability into four aspects: innovation input capability, environment support capability, self-development capability and innovation output capability, and systematically analyzes the key elements and in the composition of innovation elements and their relationships. It then constructs the evaluation index system of regional cluster innovation capability. At the same time, this paper introduces clustering analysis algorithm and swarm intelligence algorithm into regional innovation evaluation, combines particle swarm optimization algorithm and K-means clustering algorithm, and optimizes particle swarm clustering algorithm by adjusting adaptive parameters and adding fitness variance. The experimental results of this paper show that from the results of the tested innovation potential of the three industrial clusters, industrial cluster F has the strongest innovation ability, with an evaluation coefficient of 0.851, followed by industrial cluster F, which has a value of 0.623. This result is consistent with the actual innovation status of the selected industry. From this point of view, the established particle swarm clustering model for evaluating the innovation capability of regional industrial clusters is reliable and can be used to evaluate the innovation capability of different industrial clusters.

Energy-consumption calculation and optimization method of integrated system of injection-reservoir-production in high water-cut reservoir

In recent years, the world's crude-oil production has stopped increasing. Many oilfields have entered the high water-cut stage. Therefore, it is very important to reduce the energy consumption of high water-cut oilfield under limited oil production. In view of the lack of research on the overall energy consumption of the entire oilfield, three subsystems of injection, reservoir, and production are coupled through the node analysis method. The integrated energy-consumption calculation model of the injection–reservoir–production (I–R–P) coupling system is established. Finally, in order to optimize the energy consumption of the oilfield, the production scheme is optimized by combining Particle Swarm Optimization algorithm, I–R–P integration system, and reservoir numerical simulation. The optimal injection–production ratio is 1.088 in the pressure adjustment stage, the optimal target formation pressure is 15.8 MPa, and the optimal energy consumption per ton of oil is 280.15 kW h. Compared with the initial production scheme, the optimal energy consumption of I–R–P integrated system is reduced by more than 8%, which can effectively achieve the purpose of energy savings and consumption reduction. The optimization scheme has been applied in the actual field, the annual power savings could reach 6.23 × 106 kW h.

Diverse magnetism in stable and metastable structures of CrTe

In this paper, we systematically investigated the structural and magnetic properties of CrTe by combining particle swarm optimization algorithm and first-principles calculations. By considering the electronic correlation effect, we predicted the ground-state structure of CrTe to be NiAs-type (space group P63/mmc) structure at ambient pressure, consistent with the experimental observation. Moreover, we found two extra meta-stable Cmca and structures which have negative formation enthalpy and stable phonon dispersion at ambient pressure. The Cmca structure is a layered antiferromagnetic metal. The cleaved energy of a single layer is 0.464 J/m2, indicating the possible synthesis of CrTe monolayer. The \(R\bar 3m\) structure is a ferromagnetic half-metal. When external pressure is applied, the ground-state structure of CrTe transitions from P63/mmc structure to \(R\bar 3m\) structure at a pressure of 34 GPa, then to \(Fm\bar 3m\) structure at 42 GPa. We thought these results help to motivate experimental studies of the CrTe compounds in the application of spintronics.

Recovery trajectory planning for the reusable launch vehicle

Trajectory planning method for the entire recovery process of the reusable launch vehicle is studied. Firstly, a revised trajectory correction method (RTCM) is proposed to reduce the maximum normal aerodynamic load in the endo-atmosphere unpowered descent phase and improve the vehicle's landing accuracy on the influence of wind field. It consists of two parts, namely moving the ignition point of the vertical landing phase on the horizontal plane and introducing preset steady wind field into trajectory planning. Secondly, a compound trajectory planning method, which combines Particle Swarm Optimization algorithm (PSO) with Polynomial Guidance Law (PGL), is proposed to complete the optimization task of the entire recovery process. Employing PSO algorithm can avoid the complex calculation of additional angle of attack caused by the preset steady wind field in traditional trajectory correction. To overcome the premature convergence of basic PSO, the traditional re-initialization mechanism is improved. Finally, the guidance simulation of two phases greatly affected by wind is accomplished. The effectiveness of the proposed methods is demonstrated with some scenarios and Monte Carlo simulation.