Improved Particle Swarm Optimization Method Research Articles

Active micro-vibration isolation system for adaptive vibration suppression tests using piezoelectric stack actuator

An active micro-vibration isolation system for adaptive vibration suppression using piezoelectric stack actuators is presented, along with the discrete time modelling method, real-time adaptive controller design, and hardware implementation. The system comprises piezoelectric stack actuators, capacitive displacement sensors, power amplifiers, signal conditioners, and a real-time computer. A discrete asymmetric Bouc-Wen model is developed to characterize the nonlinear behavior of the piezoelectric stack actuators. System identification is conducted using an improved particle swarm optimization method, specifically the Hybrid PSO-Jaya algorithm, which sequentially integrates the PSO algorithm with the Jaya algorithm. Additionally, an improved adaptive filtering algorithm employing affine projection, referred to as the FXAPA algorithm, is introduced to facilitate real-time control. The efficacy of the proposed active micro-vibration isolation technology is validated through micro-vibration suppression tests.

Selection Process Based on Advancing the Development of a Science and Technology Law System with Chinese Features

The significance of advancing socialist S&T legal system building with Chinese features is examined in this paper, along with the extensive effects it has on S&T innovation and economic growth. Against the backdrop of an increasingly competitive global scientific and technology landscape, China’s ability to innovate and achieve sustainable growth hinges on the establishment of an ideal science and technology legislative framework. This essay first examines the primary obstacles to China’s development of a science, technology, and innovation (S&T) legal system, including inadequate protection for intellectual property rights, a flawed process for transforming scientific and technological advancements, and an insufficient system for encouraging enterprise innovation. Then, this research presents a quantitative analysis model to optimize the path of science and technology legal building by applying the improved particle swarm optimization method (PSO). The model takes into account a wide range of variables, including the degree of intellectual property protection, the strength of legal backing, the pace at which scientific and technological advancements are transformed, etc. Through the analysis of simulation data, the model also confirms the promotion effect of the legal system construction on the quantity of patent applications, the success rate of innovation projects, the enterprise R&D expenditure, and the expansion of the local economy. The study’s findings demonstrate that bolstering the science and technology legal system can effectively encourage businesses to boost R&D investment and foster regional economic development in addition to greatly raising the quantity of patent applications and the success rate of innovation projects. The rigorous intellectual property protection laws and ideal legal framework for the conversion of accomplishments greatly boost the regional innovation vitality and economic efficiency, particularly in the case study of Zhongguancun in Beijing and East China. Moreover, adaptive weighting is used to enhance the PSO algorithm and optimize the development of science and technology legal system’s comprehensive performance index, thereby confirming the model’s viability and efficacy. The study’s findings offer theoretical justification and helpful advice for China’s development of a science and technology legal framework, which is crucial for fostering innovation in these fields and boosting the country’s competitiveness.

Enhanced parameter estimation with improved particle swarm optimization algorithm for cell culture process modeling

AbstractMechanism models and model‐assisted process optimization play a vital role in biopharmaceutical industry. However, the parameter estimation remains a challenging task in modeling due to the presence of multiple local optima and ill‐conditioning. In this study, we firstly presented a comparative evaluation of particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) for parameter estimation and confirmed PSO was the most efficient approach. Then an improved PSO algorithm using second‐order oscillation and particle replacement strategies was developed to enhance fitting performance in fed‐batch cell culture modeling. The results of fitting and model validation demonstrated that the improved PSO could explore more reasonable parameters and leading to a significantly enhancement in predictive accuracy throughout the entire cell culture process under varying conditions. Moreover, 10 diverse fed‐batch experiments were conducted to validate the fitting abilities on different process condition and clones, the improved PSO method exhibited improvements in accuracy and universality of parameter estimation for modeling various cultivation processes, particularly those lacking any prior knowledge. This improved algorithm is implemented to make it available to both the scientific community and industry, offering customized solutions for specific projects.

Parameter estimation of multivariable Wiener nonlinear systems by the improved particle swarm optimization and coupling identification

This paper investigates the parameter estimation of multivariable Wiener nonlinear systems. To solve the inconsistency problem of the parameter vector and the parameter matrix, the coupling identification concept is applied. Combined with particle swarm optimization (PSO) and an auxiliary model, the partially coupled improved particle swarm optimization (PC-IPSO) method is proposed. In this algorithm, the adaptive feedback inertia weight is improved to accelerate the convergence speed, and the retirement update mechanism is introduced to improve the optimization ability of the basic PSO algorithm. To verify the performance of PC-IPSO, we also derive a multivariable improved PSO (M-IPSO) method for comparison. The computational complexity analysis shows that the PC-IPSO algorithm requires less computational resources than the M-IPSO algorithm. Then, the convergence of the improved PSO method is analyzed. The simulation results indicate that the PC-IPSO method has a faster convergence speed and higher identification accuracy than the M-IPSO and several existing state-of-the-art methods for multivariable Wiener system identification.

An Improved Particle Swarm Optimization Method for Nonlinear Optimization

Nonlinear optimization is becoming more challenging in information sciences and various industrial applications, but nonlinear problems solved by the classical particle swarm‐based methods are usually characterized by low efficiency, accuracy, and convergence speed in specific issues. To solve these problems and enhance the nonlinear optimization performance, an improved metaheuristic particle swarm optimization (PSO) model is proposed here. First, the optimization principles and model of the new method are introduced, and algorithms of the improved PSO are presented by updating the displacement and velocity of the moving particle according to Euler–Maruyama (EM) principle rather than traditional standard normal distribution. Then, the influence of the model parameters, input dimensions, and different nonlinear problems on the PSO optimization characterizations are studied by Pareto set solving and optimization performance comparison. The analysis regarding diverse nonlinear problems and optimization methods manifests that the improved method is capable of solving various nonlinear problems especially for multiobjective models, while the robustness and reliability can always keep consistent regardless of the change of model parameters. Finally, the performance evaluation is exhibited by the case study of nonlinear parameter optimization, 3 groups of CEC benchmark problems, and rank‐sum test for 6 comparable optimization algorithms, which all verify its effectiveness and reliability, as well as the significance and great application promise. The results show that the new proposed PSO method has the fastest convergence speed and least iteration numbers in searching for the global best solution of 9 nonlinear problems among 8 different optimization models indicated by the p values smaller than 0.05. Additionally, the main conclusions showing the calculation efficiency, stability, robustness, and great application promise of the proposed method are summarized, and future work is discussed.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

Application of the improved particle swarm optimization method in slope probability analysis

To improve the probability analysis efficiency of slope, this paper proposes an improved particle swarm optimization (IPSO) method to determine the critical sliding surface (CSS) and its corresponding minimum safety factor, which is used to calculate the failure probability of slope. Based on the random fields generated by using Karhunen-Loève (KL) expansion method, the simplified Bishop’s method combined with the entry and exit method, the particle swarm optimization (PSO) method, and the IPSO method is used to determine the CSS and its corresponding minimum factor of safety. Then, Monte Carlo simulation is used to estimate the failure probability of slope. The application potential of the IPSO method is demonstrated by re-analyzing two examples. Meaningful comparisons are made to demonstrate the calculating accuracy and calculating efficiency of the IPSO method in searching for the minimum safety factor of slope. Results show that the IPSO can accurately and efficiently determine the minimum safety factor in slope probability analysis considering the spatially variable soils. The IPSO method provides a promising tool for an efficient slope probability analysis.

An AI-powered approach to improving tunnel blast performance considering geological conditions

In this study, a hybrid model was developed to improve blast performance during tunnel excavation, which is based on a multilayer perceptron (MLP) and incorporates meta-heuristic algorithms. Prior to optimization, precise prediction of four post-blasting indicators is important, including overbreak area, maximum linear overbreak, crown settlement, and maximum fragment size. To identify most suitable model with the highest prediction accuracy, a comparison was conducted among three meta-heuristic algorithms, namely Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Archimedean Optimization Algorithm (AOA), in terms of their capabilities to optimize MLP hyperparameters. Since relying solely on predictions for adjusting blasting parameters is not an efficient approach, and the optimization problem of blasting parameters is consistent with the characteristics of the meta-heuristic algorithm, the optimization of blasting parameters can be performed via the combination of the optimal prediction model and meta-heuristic algorithm. Accordingly, the improved PSO method for optimizing blasting parameters can effectively reduce the overbreak areas considering the infinite number of possible combinations of blasting parameters. Compared to the previous method, overbreak areas in different sections of the tunnel were ultimately reduced by 56.1% and 35.6%, respectively. Furthermore, an importance analysis of various parameters controlling overbreak was quantitatively conducted using the developed model in combination with machine learning interpretability analysis, confirming geological conditions as the most influential factor on overbreak. Finally, validation and recommendations regarding the perimeter borehole spacing to burden ratio, which typically falls within the range of 0.6 to 1.0, are proposed. The results demonstrate that the developed model effectively improves tunnel blast performance and serves as a valuable reference for blast design.

Energy management strategy optimization for hybrid energy storage system of tram based on competitive particle swarm algorithms

Trams with energy storage are popular for their energy efficiency and reduced operational risk. An effective energy management strategy is optimized to enable a reasonable distribution of demand power among the storage elements, efficient use of energy as well as enhance the service life of the hybrid energy storage system (HESS). Thus, an energy management strategy optimization method of HESS based on a fixed power threshold is developed. Firstly, the energy management strategy optimization models based on single thresholds and multiple thresholds are developed. Then, the improved particle swarm optimization (PSO) algorithm with competing variable weights is proposed to solve optimization models. Finally, Guangzhou Haizhu tram is used to illustrate the performance of the developed method, the minimum charge state of the power battery under multiple thresholds is improved by 23.36 % over that of single threshold, and the total energy consumption of the power battery pack is reduced by 58.10 %, which shows that the energy management strategy based on multiple thresholds has better effects in giving full play to the power supply capacity of supercapacitor and reducing the energy consumption of power battery. Compared with other two methods, the improved PSO method has more reliable in utilizing more energy from supercapacitor.

The Improved Particle Swarm Optimization Method: An Efficient Parameter Tuning Method with the Tuning Parameters of a Dual-Motor Active Disturbance Rejection Controller.

Dual-motor control systems require high synchronization maintenance. Active disturbance rejection controllers (ADRC), renowned for their exceptional immunity to interference, rapid response time, and robustness, have gained widespread adoption as a prominent control strategy. The stability of the dual-motor system can be enhanced by employing an ADRC. However, setting ADRC parameters is challenging. This paper proposes an improved particle swarm optimization method (IPM) to alleviate the difficulty in parameter setting. We initially developed a simplified dual-motor ADRC model that includes current loop and speed loop ADRCs. Furthermore, aiming at maintaining synchronization of the dual-motor control system, the simplified dual-motor ADRC model and IPM method are combined. The experimental results demonstrate that in comparison with state-of-the-art methods, the proposed optimized dual-motor ADRC exhibits superior robustness, minimal overshoots, negligible steady-state errors, and high stability.

An algorithm for extracting similar segments of moving target trajectories based on shape matching

Trajectory similarity analysis of moving target is the foundation for mining high-value and regular behavioral information such as motion preferences, activity hotspots and frequent paths. Unlike most trajectory similarity analysis methods aimed at discovering correlations of target activities in time, space or spatio-temporal domains, this paper focuses on the shape matching of target trajectories. If some specific shapes frequently appear in historical trajectories, extracting these local shapes would be beneficial for analyzing the target motion templates and behavior modes. Trajectory segments with similar shapes may not have spatio-temporal correlation, and the shapes also have geometric transformation characteristics such as rotation, scaling and translation. Since the existing trajectory similarity analysis methods cannot be directly applied, an algorithm for extracting similar segments based on shape matching is proposed. First, a new shape descriptor based on signed barycenter distance (SBD) is established. It describes a trajectory as a one-dimensional shape feature sequence, which has the advantage of low computational complexity. Then, the distributed nearest neighbor search strategy is used in the particle swarm optimization (PSO) method, which aims to accelerate the retrieval of trajectory segments with similar shapes and improve the matching accuracy. Experiments on MPEG-7, handwritten character and maneuvering target simulation trajectory data sets show that compared with the existing typical shape descriptors, SBD shape descriptor has advantages in accuracy and noise insensitivity, and the improved PSO method can efficiently and accurately obtain the local shape matching results.

An experimental study on locating time-varying pollutant sources in a dynamic indoor environment with mechanical ventilation based on multi-robot cooperation

Using robots to locate indoor pollutant sources has attracted increasing attention in recent years, which is important for securing people's health and safety indoors. However, few studies have addressed the common scenarios for locating a time-varying source (release rate varies over time) in dynamic indoor environments with mechanical ventilation (condition of supply airflow varies over time). In such scenarios, the coupling effect of indoor dynamic airflow and time-varying source pose great challenges to robots locating the source. To address these challenges, in this study, a mobile robot olfaction system equipped with three robots was developed and 180 experiments in a laboratory were performed. Two methods, namely, the improved particle swarm optimization (IPSO) method and the improved whale swarm algorithm (IWOA) method, were first validated, followed by comparisons with two other methods, the standard particle swarm optimization (SPSO) method and wind utilization II (WUII) method. Both the IPSO and IWOA methods show potential for practical application due to their high success rates (averaging 78.33% and 88.33%, respectively) and localization efficiency, while the success rates of the SPSO and WUII methods (less than 50%) are insufficient for practical application.

Optimal design of a yoke-less axial flux switching PM motor based on multi-objective PSO

PurposeIn this paper, an improved multiobjective particle swarm optimization (PSO) algorithm is proposed to optimize a three-phase, 12-slot, 19-pole, yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor.Design/methodology/approachBased on the structural characteristics of the YASA-AFFSPM, a mathematical model is established to calculate the main size of the YASA-AFFSPM motor. The split ratio, stator axial length, sandwiching pole angle, rotor pole angle, PM arc and number of conductors per slot are selected as optimization variables. Also, the efficiency, power factor, cogging torque and average torque are considered as the optimization objectives. The objectives are optimized by combining the improved multiobjective PSO algorithm with electromagnetic calculation.FindingsBased on the proposed algorithm, the investigated motor is optimized. The on-load efficiency, power factor and average torque of the motor performance have increased by 0.87%, 3.13% and 10.39%, respectively. Moreover, the cogging torque and slot fill factor have undergone decreases of 8.57% and 3.34%, respectively. Finally, the effectiveness of the algorithm is verified using experiment results.Originality/valueSo far, no comprehensive report has been observed on the optimization of the YASA-AFFSPM motor using evolutionary algorithms and the study of the effect of the motor parameters. Therefore, in this paper, the authors decided to investigate the effect of YASA-AFFSPM motor parameters and improve motor performance with the improved PSO method.

Automatic shape adaptation scheme planning for CAD models in direct modeling

Designing a new CAD model by adapting a retrieved candidate model offers several advantages; however, shape adaptation is often a tough job. To facilitate shape adaptation, direct modeling (technology) provides a very flexible and intuitive approach. However, based on new design requirements, the research on automatic and effective methods to determine the geometries on the candidate model that need adapting and how to adapt these geometries has rarely been reported till date. The shape adaptation based on direct modeling is often dependent on manual processing and thus it is usually a tedious process. To account for this, a novel automatic shape adaptation scheme planning approach is proposed in this study. The candidate model is usually retrieved using a more shape-simple query model that indicates the overall shape meeting the main functional requirements of a final product (i.e., new primary shape requirements); therefore, this study focuses on the automatic adaptation of the shape of the candidate model to that of the query model to accelerate its entire adaptation progress. First, to describe the shape requirements indicated by a CAD model, an attributed face-layout graph (AFLG) is presented. Further, by comparing the candidate model’s AFLG to that of the query model, the geometries (i.e., faces) on the candidate model requiring adaptation can be determined. Next, integrating with a geometric detail reuse strategy and a novel incremental adapting method on the presented graph, an improved particle swarm optimization method is developed to determine an optimized shape adaptation scheme (i.e., an optimized direct modeling operation sequence) based on the determined geometries. Finally, by applying the above-mentioned scheme, the face layout and shape parameters are smoothly transferred from the query model to the candidate model as shape requirements, and the shape of the candidate model gets automatically adapted to the query model while its geometric details are reused adaptively. Besides, a prototype system is also implemented, and the experimental results are analyzed to verify the effectiveness and characteristics of the proposed approach. • Determining geometrics with different shape requirements by comparing new descriptors. • Generating optimized operation sequence based on a novel graph incremental adapting. • Reusing geometric details adaptively based on topology change prediction.

Optimal design of district metered areas based on improved particle swarm optimization method for water distribution systems

AbstractAlthough partitioning of water distribution systems (WDSs) into district metered areas (DMAs) is challenging, it can be effectively used for refined management and leakage control. A two-step novel process for DMA partitioning is proposed in this study, i.e. clustering and dividing. The first step is to cluster nodes through an improved METIS graph partitioning method. The second step is to optimize the location of flowmeters and gate valves on boundary pipes by obtaining the feasible solutions. The good solutions that constitute the Pareto front were produced, which could be a tough and time-consuming task. The paper proposes the innovative and efficient dividing phase: (a) selecting the important boundary pipes by hydraulic analysis; (b) using the improved particle swarm optimization algorithm; (c) proposing three objective functions. The proposed method is applied to Modena and EXNET networks to demonstrate its feasibility.

Optimal zoning for building zonal model of large-scale indoor space

Zonal model is a powerful numerical modeling approach that can quickly simulate thermal parameter distribution by dividing the indoor space into zones (zoning). However, most previous zoning methods are based on modelers’ experiences or empirical knowledge. Flow field details will be lost if the zoning is too coarse; while the computational load will be high if the zoning is too fine, especially for large-scale spaces. Therefore, this paper proposes an optimal zoning method for large-scale spaces to compromise the model accuracy and computational efficiency. In this method, air velocity and temperature differences before and after the zoning were used as indicators for accuracy. An improved particle swarm optimization method was adopted to obtain optimal Pareto solutions; and the technique for order preference by similarity to an ideal solution (TOPSIS) method was implemented to minimize the number of zones for enhancing computational efficiency. A large high-speed railway station was used as an example to verify the proposed optimal zoning method. The accuracy and computational efficiency of the proposed non-uniform zoning method were compared with a conventional zoning method and a uniform zoning method. Compared with the conventional zoning method, the proposed method reduced the number of zones by 96.2%; while critical temperature distributions and airflow patterns were well preserved. Compared with the uniform zoning method with the same number of zones, the proposed method improved the accuracy by 45.2% at most and 18.5% on average. Applicability analysis was also carried out to show that the proposed zoning method was reliable.

Non-destructive measurements of thickness and elastic constants of plate structures based on Lamb waves and particle swarm optimization

Lamb wave has become an important non-destructive measurement method due to its advantages of small attenuation, easy excitation, and simultaneous inversion of multiple parameters. According to its propagation properties, we proposed an improved particle swarm optimization method for the inversion of the thickness and elastic constants of plates in the study. Numerical simulations and validation experiments further confirmed that the Young's modulus, Poisson’s ratio, and thickness of plates could be accurately obtained from the measured zero-order modal Lamb wave dispersion curves. Then, through a series of orthogonal experiments, the influences of the error sources of measurement points on inversion parameters were explored. In addition, the statistical significance of the above influences on each inversion parameter is discussed in detail. This study provides a basis for the accurate and efficient non-destructive measurements based on Lamb waves.

Maximum power point tracking of photovoltaic array based on improved Particle Swarm Optimization Algorithm

With the vigorous development of the photovoltaic industry, how to improve the efficiency of photovoltaic power generation has become an important issue, among which partial shadow occlusion is an important reason affecting the efficiency. The efficiency of photovoltaic power generation can be effectively improved by adopting the maximum power point tracking method (MPPT), but the traditional MPPT method is not ideal in the partial shadow occlusion of the photovoltaic array. To solve this problem, this paper proposes an improved particle swarm optimization method to effectively improve the tracking efficiency of MPPT when multiple peaks appear in the photovoltaic arrays power curve (P-V) under the partial shadow. The proposed method improves the learning factor of the traditional particle swarm optimization algorithm and designs the initial position of the particles according to the characteristics of the photovoltaic array. By adding the particle elimination mechanism, the number of particles changes dynamically, and the tracking speed of the algorithm for the maximum power of the photovoltaic array is improved. Through the result of the simulation, it is not difficult to get the conclusion that the improved particle swarm optimization algorithm can effectively improve the performance of the photovoltaic system under partial shadows.

An energy-saving regulation approach based on economic rotational speed and power split feature for more electric turboshaft engine

More electric turboshaft engine is comprised of turboshaft engine and electric machines which are connected to each shaft of the engine. More electric turboshaft engine has great development potential in hybrid electric power systems in automobile and aircraft due to their high power-to-weight ratio. To reduce fuel consumption and environmental pollution, it is essential to design an energy-saving control method that can improve the fuel economy of more electric turboshaft engines within the full range of output power. In this paper, an economic rotational speed and power split feature (E-RSPSF) based compound energy-saving regulation approach is proposed for improving the fuel economy of more electric turboshaft engines. First, based on the configuration of more electric turboshaft engine, considering the power transfer balance constraint of shaft motors, augmented co-working balance equations of more electric turboshaft engine are derived by integrating a motor efficiency map. Second, a hierarchical optimization approach utilizing an improved particle swarm optimization method is presented for solving E-RSPSF based on the augmented co-working balance equations, and an E-RSPSF based regulation approach is formulated for better fuel economy of more electric turboshaft engine. Finally, the reason for the improvement in fuel economy by the E-RSPSF is discussed, and the importance of the actual motor efficiency map in the proposed E-RSPSF based regulation approach is presented. The comparison results show that the proposed energy-saving regulation approach with E-RSPSF considering both the economic rotational speed and power split can achieve a maximum 12.5% decrease in fuel consumption, which is higher than that with either rotational speed regulation or power split regulation.

Optimized Self-Adaptive Power Distribution for Microgrids in a Typical Tourism Water Village of Northern China under COVID-19 Background

This work presents an improved self-adaptive power distribution approach for the microgrid in five modes under different pandemic conditions in a typical tourism water village in Northern China. Differently from the other studies, this work concentrates on satisfying the specific power supply requirements under the COVID-19 background, with the maximum value of the composite index as the object function. Composite index includes not only the economic factors, but also some compulsive factors to ensure the requested power supply of the residents/tourists. The improved particle swarm optimization method which employs the modified weighted factor and the elite strategy is utilized to optimize the power dispatching of the microgrid. Moreover, the impact of the pandemic has been fully considered by comparing the power dispatching before and after the pandemic. The case study in Baiyangdian Region confirms the effectiveness of the proposed method. With this method, the optimal power dispatching is determined under different modes.