Adaptive Particle Swarm Optimization Research Articles

Energy management strategy for low hydrogen consumption in hybrid power systems consisting of dual fuel cells and a single lithium battery

ABSTRACT Low hydrogen consumption is an important objective for fuel cell power systems as it impacts both economic efficiency and continuous power supply. This paper addresses the optimization problem of minimizing hydrogen consumption in a hybrid power system comprising dual fuel cells and a single lithium battery. We introduce an energy management method for the multi-period synchronous optimization, incorporating a data processing module and power allocation module. The quasi-periodic loads are predicted using a modified gray-Markov chain model, while the optimization problem is solved using an adaptive constrained particle swarm optimization (ACPSO) algorithm. An initial swarm generation algorithm is introduced to enhance the effectiveness of ACPSO, and a power correction algorithm is integrated to address deviations between predicted and actual loads. The effectiveness and advantages in optimizing hydrogen consumption of the proposed strategy have been demonstrated through simulations and experiments.

A Particle-Swarm-Optimization-Algorithm-Improved Jiles–Atherton Model for Magnetorheological Dampers Considering Magnetic Hysteresis Characteristics

As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles–Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.

An adaptive binary quantum-behaved particle swarm optimization algorithm for the multidimensional knapsack problem

The multidimensional knapsack problem (MKP) is a classical combinatorial optimization problem with wide real-life applications. Binary quantum-behaved particle swarm optimization (BQPSO) algorithm is a popular heuristic algorithm used in binary optimization. While BQPSO exhibits strong global search capabilities, it is still prone to local optima due to particle aggregation. To address this issue, an adaptive BQPSO (ABQPSO) algorithm is proposed to solve the MKP efficiently. A hybrid encoding population initialization scheme is employed, leveraging specific knowledge of MKP to increase population diversity and improve search efficiency. Furthermore, ABQPSO uses a mapping strategy that converts continuous values into discrete values based on the average position of particles. An adaptive repair operator considering two pseudo-utility ratios is introduced to enable particles to explore different feasible regions, which dynamically adjusts current pseudo-utility ratios based on changes in the global best solution. A local search method is applied to guide particles towards convergence to the optimum. A local sparseness degree measurement and a diversity mechanism are utilized to avoid local optima. To evaluate the effectiveness of ABQPSO, it is compared against ten state-of-the-art algorithms using 168 MKP benchmark instances of varying scales. Experimental results reveal that ABQPSO outperforms the comparison algorithms, especially for large-scale problems, demonstrating better solution accuracy.

Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.

Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm

In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path. To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest. Especially in the complex level 4 wave water environment, the AIPSO algorithm had the best adaptability and robustness, and the robot had the highest working efficiency and comprehensive performance. The experimental results revealed that the AIPSO algorithm effectively improved the path search and garbage cleaning efficiency of the robots and reduced the working time, which further verified the reliability and accuracy of the designed algorithm.

A sequential three-way decision model for classification with multilevel information gain and regret value optimization

The sequential three-way decision (S3WD) model plays a vital role in solving classification problems. This model comprises three fundamental elements, namely, conditional probability, cost function, and decision thresholds. The typical conditional probability estimation methods are bifurcated into two branches: equivalence class-based methods, which coincide with human recognition, and machine learning-based methods that rely on big data for high precision. This study synergizes two branches by introducing a novel algorithm that integrates the K-nearest neighbor method and Bayes rule for estimating conditional probabilities, enhancing both interpretability and precision. Regarding the cost function and decision thresholds, the inherent weak point in existing cost-sensitive S3WD models is the computation of three pairwise adjacent regions with subjectively given cost values and decision thresholds. To overcome the subjective arbitrariness bottleneck, an objective function is established by extending the definitions of information gain and regret value across a multilevel granularity structure. Subsequently, an adapted particle swarm optimization algorithm is utilized to optimize the decision thresholds and tri-partitions. Accordingly, a novel framework of the S3WD model is constructed. Finally, extensive experimental results substantiate the effectiveness and superiority of the proposed model in the realm of classification, as evidenced from both technical and empirical standpoints.

Avoiding stick-slip limit cycle vibration in electric vehicle brake system via brake force distribution

Brake creep groan, a friction-induced nonlinear vibration in the brake system, has significantly contributed to the reduction of ride comfort. In order to mitigate brake creep groan, a brake force distribution strategy based on the stick-slip curve is proposed and examined. The critical boundary of the stick-slip limit cycle is determined and established as the constraint condition. Focusing on avoiding stick-slip limit cycle vibration, an adaptive particle swarm optimization method is employed to study the distribution of brake force between regenerative brake and friction brake, with energy recovery efficiency serving as the objective function. Four distinct operational conditions—NEDC, UDDS, WLTP, and FTP—are simulated. The results demonstrate that under these conditions, vibrations associated with the stick-slip limit cycle can be reduced by up to 23.596%, 15.368%, 15.342%, and 17.557%, respectively. Considering the peak power and peak torque of the drive motor, three regions can be identified: the region where stick-slip limit cycle vibration cannot be avoided, the region where avoidance efficiency increases with the rise in the drive motor’s peak torque and peak power, and the region where avoidance efficiency remains constant despite increases in the drive motor’s peak torque and peak power.

Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO

Collaborative operations of multiple AUVs have been becoming increasingly popular and efficient in underwater tasks of marine applications. Autonomous navigation capability and cooperative control stability of multiple AUVs are crucial and challenging issues in underwater environments. To address the collaborative problem of path planning for multiple AUVs, this paper proposes an adaptive multi-population particle swarm optimization (AMP-PSO). In AMP-PSO, we design a grouping strategy of multi-population and an exchanging mechanism of particles between groups. We separate particles into one leader population and various follower populations according to their fitness. Firstly, in the grouping strategy, particles within the leader population are updated by both the leader population and follower populations so as to keep global optimization, while particles within the follower population are updated by their own group so as to keep local priority. Secondly, in the exchanging mechanism, particles are exchanged between the leader population and follower populations so as to improve multi-population diversity. To accommodate multi-population characteristics, an adaptive parameter configuration is also included to enhance the global search capability, convergence speed, and complex environment adaptability of AMP-PSO. In numerical experiments, we simulate various scenarios of collaborative path planning of multiple AUVs in an underwater environment. The simulation results convincingly demonstrate that AMP-PSO can obtain feasible and optimal path solutions compared to classic PSO and other improved PSO, which enable multiple AUVs to effectively achieve objectives under the conditions of collision avoidance and navigation constraint.

Structure design and advantage research for bidirectional synchronous force-increasing EMB actuator

Electro-Mechanical Brake (EMB) can provide an ideal brake actuator for the active braking system of intelligent vehicles and the decoupled regenerative braking system of new energy vehicles. In order to solve the problem of large volume, weight, and axial size of the existing EMB actuator, a novel bidirectional synchronous force-increasing EMB (BSF-EMB) actuator scheme is proposed. Based on the maximum required braking force of the target vehicle under emergency braking conditions and the limitation of installation space in the wheel hub. The rotary motor and the ball screw are selected, the force-increasing mechanism parameters calculation and structure design are completed. In order to minimize the difference between the force-increasing ratio of the two-stage force-increasing mechanism and the lever force-increasing mechanism. The parameters of the force-increasing mechanism are optimized based on the weight adaptive particle swarm optimization algorithm. The three-dimensional model of the EMB actuator is established. In addition, when the required braking force is the same, the mass and volume of the BSF-EMB actuator can be decreased by about 43.4% and 55.8%, compared with the existing planetary gear force-increasing EMB (PGF-EMB) actuator scheme. Therefore, the BSF-EMB actuator can effectively decrease the unsprung mass of the vehicle and resolve the issue of limited installation space in the wheel hub of compact vehicles. The EMB models for two EMB actuator structures are built based on MATLAB/Simulink, and the EMB braking response speed and tracking performance of braking force are compared. The simulation results show that the braking response speed of the BSF-EMB actuator can be increased by 11%, 10.6%, 12.7%, and 14% compared to the PGF-EMB actuator under emergence braking condition and other three general service braking conditions, and the braking force adjustment is more precise. Finally, the effectiveness for the control strategy is verified by hardware in loop (HIL) experiment under sin wave input condition for braking force. The related research can provide reference for the design and application of EMB.

Shape detection of thin diameter flexible sensors with non-uniform gratings

Flexible intelligent medical device shape feedback has a very important application prospect in intelligent structure detection. Due to the error accumulation effect, the distribution of gratings is also an important factor affecting the reconstruction error of shape sensors. A grating positions optimization method based on the fusion of adaptive mutation particle swarm optimization algorithm and shape reconstruction algorithm is proposed in the paper. The curves with 10 measurement points arranged at a sensing length of 810 mm and 4 measurement points arranged at a sensing length of 200 mm are simulated and calculated. After multiple optimization training, the sparse optimal distribution of gratings with the smallest shape error is obtained. Package sensors with uniform and non-uniform distribution of grating points based on the obtained point positions, and conduct planar shape reconstruction experiments. After multiple shape reconstruction experiments, the shape errors of the sensor shape reconstruction endpoint for uniform gratings are 2.76% and 2.94%, respectively, and the reconstruction accuracy for non-uniform gratings is 1.94% and 2.71%. The optimized shape sensor has good performance, thus proving the effectiveness of the fiber grating sensing grid point optimization method in flexible medical device deformation monitoring.

Station layout optimization for underwater acoustic positioning system based on combined cone configuration

The theoretical minimum of the geometric dilution of precision (GDOP) for target positioning cannot be achieved because of the coplanar constraint for underwater station configurations, and the conventional single-cone configuration leads to an infinite GDOP. Therefore, this study extends the single-cone configuration to the combined cone configuration. The proposed configuration is theoretically proven to be superimposable, rotatable, and GDOP-rotation-invariant. A criterion based on the global GDOP for underwater targets is constructed to address the problem of a single criterion for station layout optimization. However, traditional optimization algorithms are prone to local optimality and low convergence efficiency. Therefore, an improved adaptive particle swarm optimization (APSO) algorithm is proposed to optimize the configuration of the underwater acoustic positioning systems. Finally, it is verified that the proposed APSO can enhance the global optimality and improve the convergence efficiency of the algorithm. Combined with the measured data of the long baseline system, it is shown that the optimal combined cone configuration significantly reduces the global GDOP and improves the positioning accuracy compared with traditional configurations.

Multi-model fusion stacking ensemble learning method for the prediction of berberine by FT-NIR spectroscopy

Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its quality. Fourier transforms near-infrared (FT-NIR) spectroscopy is a commonly used non-destructive method for rapidly detecting berberine content. In contrast to single-supervised learning algorithms in machine learning, ensemble learning combines individual learning algorithms to create a stable and better-performing strong-supervised model. This study collected spectral data of Rhizoma Coptidis using FT-NIR spectroscopy technology and established a chemometric model using a stacking ensemble approach with multiple models. Partial Least Squares (PLS), Adaptive Boosting (AdaBoost), Gradient boosting decision trees (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) regression models were chosen as alternative base models, different Stacking models were established by random combinations. To fully leverage the strengths of each model and enhance predictive capability, an adaptive inertia weight particle swarm optimization algorithm (AWPSO) was used to search for the optimal parameters. The correlation coefficient of the test (RT) and the root mean square error of the test (RMSET) systematically evaluated the model performance. Finally, AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were selected as the base models. The RMSET and RT for RF, XGBoost, and AdaBoost were 0.226, 0.250, 0.195, and 0.871, 0.830, 0.927. After optimizing with the AWPSO algorithm, the RMSET and RT for AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were 0.226, 0.245, 0.194, and 0.871, 0.843, 0.922, respectively. The RMSET and RT values for the stacking ensemble were 0.174 and 0.932. The prediction accuracy and generalization ability of multi-model fusion stacking ensemble learning are superior to the single-model regression methods. Therefore, the stacking ensemble learning method that combines AdaBoost, RF, and XGBoost regression models is effective and feasible for assisting in the detection of berberine content in Rhizoma Coptidis.

Hierarchical multi-objective optimization of proton exchange membrane fuel cell with parameter uncertainty

It is important to improve the performance indexes including the net power and system efficiency for polymer electrolyte membrane fuel cell (PEMFC), especially considering the parameter uncertainty. To this end, this paper proposes a hierarchical multi-objective optimization framework including the off-line and on-line optimization. Firstly, a steadystate non-linear PEMFC system model is established as the base model. Secondly, an adaptive multi-objective particle swarm optimization (AMPSO) algorithm scheme is proposed in the off-line optimization procedure for the exact optimization of operation parameters. Meanwhile, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Finally, to decrease the influence caused by parameter uncertainty, the interval optimization method is proposed in the on-line optimization layer based on the results of AMPSO. The convergence condition of the proposed optimization scheme is verified by theory analysis. The proposed AMPSO is compared with different algorithms in the numerical simulation and hardware-in-loop (HIL) experiments. Meanwhile, the performance of the proposed method is tested on four representative benchmark problems. These results demonstrate that the PEMFC system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the net power and system efficiency indexes, revealing the success of this hierarchical optimization approach in solving the accurate optimization and parameter uncertainty problems. By using statistical test methods, the proposed algorithm performs better hypervolume (HV) metric from the Pareto solution distribution.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework

Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction.

Design and Development of Hybrid Microgrid Control System for Renewable Energy Generation

Renewable energy sources are widely acknowledged as the optimal substitutes for conventional energy outlets globally. Countries are endowed with diverse renewable resources, including solar, wind, biomass, hydro, and tidal energy. Despite this abundance, there exists a substantial disparity between the demand and supply of electrical energy, with numerous regions still facing insufficient access to power. The integration of various renewable energy sources in remote and isolated locations forms a Microgrid (MG), catering adequately to local energy requirements. These microgrids have the capability to function seamlessly alongside conventional grids. Hybrid MG system, incorporating Photovoltaic (PV) with battery storage and a Wind Turbine (WT), emerges as a practical solution for electrifying remote areas in islanded mode. The WT’s installed capacity is chosen to meet critical load requirements during periods of non-availability of renewable sources. The battery's capacity is selected to cover the transition period between renewable sources or as a backup source, ensuring cost-effectiveness over WTs. The application of Adaptive Particle Swarm Optimization (APSO) has demonstrated favorable outcomes compared to existing PSO algorithms, enhancing control efficiency. In a grid-connected MG system, the integration of distributed PV generation has notably improved the voltage profile, reducing overall losses. Consequently, the presented MG systems provide both technically and economically viable solutions for ensuringcontinuous electricity supply in isolated and grid-connected systems. This approach not only mitigates pollution but also allows for the possibility of capacity addition, fostering sustainable growth in these regions.

Research on innovative methods of virtual reality course content for tourism education

Abstract Tourism practice teaching is an important means to cultivate high-quality skilled tourism talents. When virtual reality is in tourism practice, teaching undoubtedly shows a beautiful blueprint for modern tourism education. The article demonstrates the advantages of the application of VR technology, designs a VR practical training system for tourism education, and develops VR resources and interface interaction design for tourism education. Based on the binocular stereo imaging model to obtain maximum and minimum parallax, and combined with the adaptive weight PSO algorithm for optimizing tourism virtual scene viewpoints. Then, a comparison test for teaching was designed using the VR training system for tourism education. By analyzing students’ eye movement data to understand the viewpoint optimization effect of the AW-PSO algorithm, we also verified their learning performance, learning experience, and academic mood changes. The edge strength and clarity of the virtual scene obtained by the AW-PSO algorithm were 67.06 and 14.71, respectively, and the theoretical score of the experimental class students was 83.15, which was 9.06% higher than that of the control class students. Based on tourism education, VR practical training systems can effectively enhance students’ learning interest and academic mood for tourism education courses and help develop high-quality tourism education courses in colleges and universities.

Adaptive Particle Swarm Optimization Algorithm and Application Model Based on Diversity-Driven Optimization

Adaptive Particle Swarm Optimization Algorithm and Application Model Based on Diversity-Driven Optimization