Improved Particle Swarm Optimization Research Articles

Retraction Note: effective heart disease prediction using improved particle swarm optimization algorithm and ensemble classification technique

Retraction Note: effective heart disease prediction using improved particle swarm optimization algorithm and ensemble classification technique

A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy

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

Optimal Scheduling of Island Microgrids with Seawater Pumped Storage Plants for Multi‐Energy Complementarity

The rapid development of new energy sources, such as offshore wind power and photovoltaic power, has provided a new solution to the problem of power supply for islands far from the mainland. Wave energy is a kind of renewable energy originated from the ocean, but the existing island power supply programs seldom consider this favorable natural condition. In addition, seawater variable‐speed pumped storage is a new idea to consume offshore wind power and improve the reliability of coastal and island power systems. In view of the stochastic and intermittent nature of new energy sources, this paper adopts seawater variable‐speed pumped storage power plants as energy storage equipment, and put forward an island power supply scheme with wind power, photovoltaic power generation, wave power generation, pumped storage power plants and diesel generator sets as a multifunctional complementary isolated grid. Firstly, wave energy generators, wind farms, photovoltaic farms, pumped storage power stations and diesel generator sets are modeled separately. Then, considering their respective operating conditions, constraints and load requirements, the optimal scheduling of island microgrids with multi‐energy complementarity is constructed. Finally, based on the improved particle swarm optimization algorithm, the model is solved. According to the wind power photovoltaic and wave power output curves of several typical scenarios in an island far away from the mainland, the cost and benefit of different schemes are compared to verify the effectiveness of the optimal scheduling in this paper. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Safe path planning of mobile robot based on improved particle swarm optimization

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

Optimum texture shape under different lubrication conditions applied to the start-up phase of journal bearings

This paper presents a parameterized model to optimize the texture units using an improved particle swarm optimization. The texture units are optimized under three lubrication states: boundary lubrication, mixed lubrication, and hydrodynamic lubrication, by utilizing the Stribeck curve. The optimal texture units can be applied to journal bearings and thrust bearings in different operating conditions to achieve maximum load-carrying capacity. The paper applies the optimal textures under the three lubrication states to the start-up stage of journal bearings to examine the lubrication performance and dynamic characteristics of the textured journal bearing system. The results demonstrate that compared to the untextured journal-bearing system, the introduction of optimal textures improves the tribological performance of the journal-bearing system, and the optimal textured units under hydrodynamic lubrication have the best friction reduction effect. Furthermore, it is found that the introduction of textures has no significant effect on the dynamic characteristics of the journal bearings.

Configuration design of a steel double lazy wave riser based on metamodel-assisted metaheuristic algorithms

This study presents an approach to the configuration design of a steel double lazy wave riser. Considering the length of each segment and arrangement parameters of buoyancy blocks as design variables, the objectives are to minimize the maximum static stress, horizontal motion scope, and maximum standard deviation of dynamic stress, respectively. A self-developed program was established to calculate the riser dynamics in a fully coupled platform/mooring/riser system. An improved Particle Swarm Optimization (PSO) that can handle both continuous and discrete design variables was proposed to automatically find the optimal solution. Feedforward Neural Networks (FNNs) corresponding were embedded into the optimization algorithm to replace the time-consuming numerical calculation. The results show that the FNN provides high accuracy with an error of no more than 6.34 % and all Determination Coefficients of about 0.95. The static-stress optimization tends to reduce the arch height in the riser; while the dynamic optimizations tend to increase the arch heights. Three optimizations all reach feasible solutions with noticeable objective reductions.

Financial distress prediction using an improved particle swarm optimization wrapper feature selection method and tree boosting ensemble

Financial distress prediction (FDP) is a complex task involving both feature selection and model construction. While many studies have addressed these challenges individually, there is a lack of FDP models that integrate feature selection into the overall model building process. To address the issues of fast convergence and local optimization in particle swarm optimization (PSO), this study proposes a modified PSO algorithm with three improvement strategies. This improved PSO, known as IPSO, is embedded within state-of-the-art tree boosting ensemble models for feature selection. IPSO continuously evolves toward the optimal feature subset, preventing premature convergence and avoiding local optima. To construct the FDP model, four state-of-the-art tree boosting models are combined using an improved majority voting ensemble strategy. The integration of IPSO enhances the prediction performance of the model. Comparative experiments are conducted on samples of Chinese listed companies under short-term and long-term prediction scenarios to evaluate the proposed model. The results demonstrate that IPSO significantly improves the prediction performance, and the ensemble strategy enhances the robustness of the predictions.

Research on Agricultural Product Price Prediction Based on Improved PSO-GA

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers’ planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of “high in winter and low in summer”; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products.

Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives

AbstractCarbon emissions limit the output of traditional fuel‐fired generating units, significantly affecting the new power system scheduling mechanism. This paper proposes a short‐term electric power and energy balance optimization scheduling method with low‐carbon bilateral demand response (LCBDR). The LCBDR mechanism framework is constructed by combining the analysis of short‐term electric power and energy balance of the system under a dual perspective, along with the electric‐carbon coupling mechanism of the dynamic scheduling on the source‐load side. Based on the carbon emission flow (CEF) theory, the carbon emission index information of load‐side users is obtained. An optimal scheduling model of LCBDR is established. The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. Comprehensive case studies from three different perspectives verify that this method can effectively realize the low‐carbon economic operation of the system, with the peak net load reduced by 24.02% and valley net load increased by 20.43%. Compared with a single perspective, the total operational costs can be reduced by 5.27%, and the carbon emissions of users can be reduced by 5.70%.

Mutual inductance identification of non-contact excitation system

Due to the presence of brush and slip ring in the excitation method of electrically excited synchronous motors, this article proposes a new excitation method - non-contact excitation system. This method transfers electrical energy from the stator to the rotor through magnetic coupling, replacing slip ring and brush. However, the magnetic coupling coils at the primary and secondary ends of the system will deviate, which will affect motor operation quality. In order to effectively reduce the changes caused by mutual inductance, this article proposes an improved particle swarm optimization algorithm for mutual inductance identification. This improved algorithm can effectively reduce the shortcomings of low accuracy and easy to fall into local optima in particle swarm optimization. Simulation and experimental results show that the improved particle swarm optimization algorithm can improve search accuracy.

Identification of low voltage ride-through parameters of DFIG based on improved particle swarm optimization algorithm

Identification of low voltage ride-through parameters of DFIG based on improved particle swarm optimization algorithm

Line optimization of multi-beam sonar sounding technology in ocean surveying and mapping

Abstract In this paper, the problem of line optimization existing in the practical application of multi-beam sonar-sounding technology is studied. Firstly, by analyzing the working principle of multi-beam sonar, the multi-beam coverage width model and the overlap rate model between adjacent strips are established considering the slope of seabed terrain. Then, a more general parameterized multi-beam coverage width formula with an adjustable azimuth angle of the line is derived under three-dimensional space coordinates. Then, the minimum total line length is defined as the objective function, and the constraint conditions of overlap rate and complete coverage are added to establish the mathematical model of line planning. Then, an iterative algorithm is designed to quickly determine the line spacing, and an improved particle swarm optimization algorithm, including a trigonometric function change learning factor, is used to search for the optimal line layout. Finally, the simulation results show that the optimized scheme can reduce the total line length by 2% compared with the unoptimized line layout, which verifies the effectiveness of the established model and algorithm. This study provides a theoretical basis for navigation planning and shipboard system optimization of multi-beam sonar survey. Subsequent work taking into account the influence of complex sea conditions, efforts were made to expand the model’s applicability.

Improved PSO-Based Two-Phase Logistics UAV Path Planning under Dynamic Demand and Wind Conditions

Unmanned aerial vehicles (UAVs) have increasingly become integral to logistics and distribution due to their flexibility and mobility. However, the existing studies often overlook the dynamic nature of customer demands and wind conditions, limiting the practical applicability of their proposed strategies. To tackle this challenge, we firstly construct a time-slicing-based UAV path planning model that incorporates dynamic customer demands and wind impacts. Based on this model, a two-stage logistics UAV path planning framework is developed according to the analysis of the customer pool updates and dynamic attitudes. Secondly, a dynamic demand and wind-aware logistics UAV path planning problem is formulated to minimize the weighted average of the energy consumption and the customer satisfaction penalty cost, which comprehensively takes the energy consumption constraints, load weight constraints, and hybrid time window constraints into consideration. To solve this problem, an improved particle swarm optimization (PSO)-based multiple logistics UAV path planning algorithm is developed, which has good performance with fast convergence and better solutions. Finally, extensive simulation results verify that the proposed algorithm can not only adhere to the UAV’s maximum load and battery power constraints but also significantly enhance the loading efficiency and battery utilization rate. Particularly, compared to the genetic algorithm (GA), simulated annealing (SA), and traditional PSO strategies, our proposed algorithm achieves satisfactory solutions within a reasonable time frame and reduces the distribution costs by up to 9.82%.

An optimal dispatch strategy of off-grid park integrated energy system considering source volatility

An off-grid integrated energy system (IES) with hydrogen storage at park-level is proposed, utilizing wind, solar and natural gas as the main energy supply to replace fossil fuels, in order to overcome the insufficient consideration of energy source conversion and information exchange in the traditional energy system. Firstly, an IES schematic consisted with typical devices is proposed, and all the components are modelled in detail. Then, based on the renewable energy (RE) input requirements and the facility parameters, an optimal dispatch model for multi-energy conversion based on hydrogen storage has been demonstrated. In addition, an improved particle swarm optimization algorithm is also introduced to improve the strategy. Combined with the environmental characteristics, the multi-objective solution with the lowest economic cost and environmental cost, the highest energy supply reliability and energy efficiency as the optimization objectives is determined. Finally, a case study is conducted using the practical data of the Chongli Large-Scale Wind-Solar Complementary Coupled Hydrogen Production System Application Demonstration Project to validate the feasibility of the study under real conditions. The result shows that the RE consumption rate is greater than 99 % under the premise of considering the environmental cost and ensuring the operation reliability, which can effectively improve the operation economy and user economy of the power grid

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

Hybrid Path Planning Strategy Based on Improved Particle Swarm Optimisation Algorithm Combined with DWA for Unmanned Surface Vehicles

Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.

Economic evaluation of the second-use batteries energy storage system considering the quantification of environmental benefits

The number of retired batteries from electric vehicles continues to grow, which not only puts pressure on battery recycling but also leads to environmental pollution. By replacing the conventional batteries with the second-use batteries in configuring the energy storage system, the investment cost can be effectively reduced, and the issue of retired batteries disposal can be alleviated. In view of this, the paper investigates the quantification of the environmental benefits of second-use batteries, and comprehensively evaluates the second-use batteries energy storage system (SUBESS) in terms of environmental and economic benefits. The energy storage configuration model aimed at maximizing annual net income is developed, and the improved particle swarm optimization algorithm is applied to determine the optimal capacity and power in a photovoltaic (PV) pilot area. Under the same capacity condition, several evaluation indexes are used to compare the economics of the SUBESS with the conventional batteries energy storage system (CBESS). The results show that: (1) Compared to end-of-life disposal of batteries, secondary utilization will yield greater environmental benefits. (2) When the cost per unit capacity of the SUBESS is lower than 63.99 % of the CBESS, the economy of the SUBESS is better than that of the CBESS. Finally, targeted suggestions are made to further promote the scale development of the retired batteries secondary utilization industry. The research results not only validate the economic feasibility of the SUBESS but also have significant environmental benefits, which can provide a reference for potential investors.

Kinematic Parameter Identification for a Parallel Robot with an Improved Particle Swarm Optimization Algorithm

Kinematic Parameter Identification for a Parallel Robot with an Improved Particle Swarm Optimization Algorithm

Application of the artificial neural network and enhanced particle swarm optimization to model updating of structures

AbstractAn efficient and accurate two-stage approach, based on the artificial neural network (ANN) and an enhanced particle swarm optimization (EPSO) approach for model updating of structures using incomplete measurements, is proposed in this study. The first stage, preliminary model updating, employs the ANN to quickly learn the mapping relationship between the natural frequencies and stiffness of the structure using a few training, validation, and testing instances. The inputs and outputs of the ANN are the natural frequencies and stiffness of the structure, respectively. The ANN’s training, validation, and testing instances are extracted through Latin hypercube sampling. The ANN-predicted stiffness provides an excellent basis for determining and reducing the search space of the optimal stiffness in the second stage. The second stage, detailed model updating, searches for the optimal stiffness of the structure by using the EPSO approach. The EPSO approach improves particle swarm optimization (PSO) by employing an elite crossover strategy to avoid trapping in the local optimum and premature convergence. The feasibility and effectiveness of the proposed two-stage approach for stiffness updating of shear building structures using incomplete measurements are demonstrated by numerical and experimental examples. The results present that the proposed two-stage approach improves the computational efficiency and solution quality of the GA (Genetic Algorithm) and PSO for stiffness updating of shear building structures.

Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging.

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods.