Local Particle Swarm Optimization Research Articles

Optimizing energy management strategies for microgrids through chaotic local search and particle swarm optimization techniques

The advent of multi-Microgrid (MG) energy systems necessitates the optimization of management strategies to curtail operational costs. This paper introduces an innovative MG energy management strategy that integrates Chaotic Local Search (CLS) with Particle Swarm Optimization (PSO) to fulfill this requirement. Our approach leverages PSO for extensive global exploration and subsequently employs CLS to refine local searches, thereby ensuring the attainment of optimal global outcomes. To further enhance performance, we have crafted a PSO algorithmic framework underpinned by chaotic local search principles, aimed at circumventing regions of local optima. The study presents a comprehensive MG energy system model that encompasses a photovoltaic generation unit, battery energy storage, and a micro gas turbine. The experimental data corroborates that our proposed algorithm secures optimal solutions within a range of 48.2–51.7, outperforming others in achieving these optimal resolutions. When juxtaposed with Scenario 1, there is a significant reduction in both operational and primary energy conversion costs by 24.22 % and 31.39 %, respectively. In comparison to Scenario 2, these figures are reduced by an additional 3.08 % and 6.05 %, respectively. The research findings underscore the strategy's exceptional performance in optimization tasks, as illustrated by the simulation outcomes. The methodology's application to a micro-energy network substantiates its practical relevance. Collectively, this research offers a holistic solution for the optimization of MG energy systems, effectively merging theoretical progress with tangible practical applications.

Development of CuS/C composite for microwave absorption using lignin biopolymer

In recent decades, electronic devices have become a crucial component of everyday life and an increasingly important aspect of national security. However, despite their convenience, the emission of electromagnetic (EM) radiation from these devices has created a pollution issue that demands urgent attention. In this study, the properties and performance of CuS/C composites as EM wave-absorbing materials were examined. The main technique employed involved modifying the surface structure and properties of a lignin biopolymer via a fiber laser to derive porous carbon as a microwave absorber in the X-band range. Flower-like CuS microspheres were integrated into the porous carbon to improve the electrical conductivity and promote internal reflections. In addition, multilayer microwave absorbers were developed using CuS/C/epoxy composites with varying weight percentages, which were fabricated through refluxing and annealing techniques. The return losses of the absorbers in single- and multi-layer modes were optimized by modified local particle swarm optimization (MLPSO). The minimum average return loss improved to −15 dB (96.8%) by optimization in the 4-layer mode with a thickness of 1.8 mm. The optimum absorption peak in the 3-layer structure was −54.52 dB at a frequency of 8.83 GHz. The shielding effectiveness assessments indicated that the CuS/C/epoxy composite significantly improved the shielding capabilities of porous C and CuS, resulting in an increase of the shielding effectiveness threshold (SET) from less than 2 dB in lignin to more than 10 dB in CuS/C/epoxy.

Application of Local Search Particle Swarm Optimization Based on the Beetle Antennae Search Algorithm in Parameter Optimization

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

Comparing Optimized Sound Absorption Coefficient of Aluminum Foam with Local Search Algorithm, Genetic Algorithm, and Particle Swarm Optimization

Introduction: The principle of passive sound control is based on the phenomenon of sound absorption by absorbers. The factors affecting sound absorption include porosity, pore size, pore opening size, thickness, and air flow resistance. Materials and methods: In this study, the authors compared the optimization results of the effective parameters on sound absorption coefficient (AC) using the three optimization methods: Guided Local Search (GLS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The programming was done in MATLAB software. Thicknesses of 5, 10, 20, 30 and 40 mm were chosen for optimization at frequencies of 500 to 3000 Hz. Results: In frequencies above 2 kHz (thickness 5 to 40 mm), the three optimal methods had the same performance and estimated AC of 1. At low frequencies of 2 kHz and thicknesses of 30 and 40 mm, GA and PSO methods obtained an AC of 1. Conclusion: It seems that the GA and PSO optimization algorithm are suitable methods to optimize the AC of metal foam in low and high frequencies.

AdaL-PSO A New Adaptive Algorithm for the Multi-Skilled Resource-Constrained Project Scheduling Problem

MS-RCPSP is a combinatorial optimization problem that has many practical applications, this problem has been proven to belong to the NP-hard class, the approach to solving this problem is to use algorithms to find approximate solution. This paper proposed a New Adaptive Local Particle Swarm Optimization algorithm for the MS-RCPSP problem. The solution for the class of NP-Hard problems is to find approximate solutions using metaheuristic algorithms. However, most metaheuristic-based algorithms have a weakness that can be fallen into local extreme after a number of evolution generations. In this paper, we adopted a new adaptive nonlinear weight update strategy based on fitness value and new neighborhood topology for Particle Swarm Optimization algorithm, thereby helping to prevent PSO from falling into local extremes. The new algorithm is called AdaL-PSO. A numerical analysis is carried out using iMOPSE benchmark dataset and is compared with some other early algorithms. Results presented suggest the prospect of our proposed algorithm.

Enhancing Sustainability and Efficiency in Offshore Oil and Gas Engineering through the Integration of Chaotic Local Search and Particle Swarm Optimization for Microenergy Systems Optimization

The offshore oil and gas industry is under increasing pressure to reduce carbon emissions while maintaining energy reliability. Offshore oil and gas platforms (OOGPs) face significant challenges in integrating low‐carbon operations with their energy systems. This study introduces an optimized scheduling approach for offshore microintegrated energy system (OMIES) that incorporates a hybrid energy storage system, including a floating power‐to‐gas associated gas storage (FP2G‐AGS) module, to address the intermittency of renewable energy sources. An economic optimization model is formulated, accounting for carbon emissions, operational costs, and the status of gas turbine generator sets. To solve the complex optimization problem, this study develops a hybrid chaotic local search and particle swarm optimization (CLPSO) algorithm. The CLPSO algorithm synergizes the global search ability of PSO with the local refinement of chaotic local search, enhancing the convergence to optimal solutions. Experimental results demonstrate that the proposed CLPSO algorithm effectively achieves optimal solutions within a range of 48.2–51.7. Case studies validate the model’s capability to promote new energy integration, reduce operational costs, and decrease CO2 emissions across various scenarios. This research significantly contributes to achieving low‐carbon operations on OOGPs and promotes the sustainable development of marine resources.

Embedded Feature Selection Approach Using Penalized Logistic Regression for Universal Steganalysis

For universal steganalysis, the Spatial Rich Model (SRM) provides a robust feature set in the form of 106 feature submodels (34671-dimensional) to attack highly secure steganography methods. Nonetheless, the curse of dimensionality emerges as a drawback, prompting researchers to explore feature selection methods to achieve optimal performance for steganalyzers. In previous work, a wrapper feature selection method using global and local best particle swarm optimization (GLBPSO) has been used to select 28 submodels of SRM out of 106 submodels. The 28 submodels contain a total of 9152 features and an ensemble classifier has been used for the classification results. In this study, an embedded feature selection and classification approach using logistic regression (LR) with an elastic net penalty has been proposed and applied to the 9152 features. The proposed approach efficiently selects an informative subset of 9152 features while training the LR classifier. This not only reduces the number of features by a significant 75% but also boosts the classification performance when contrasted with the ensemble classifier. The proposed approach achieves an accuracy of 87.41%, surpassing the current state-of-the-art steganalysis methods based on deep learning by a substantial margin of 3-7%. It also outperforms a recent machine learning-based steganalysis method by 13%.

An adaptive GLNPSO method for inventory replenishment supply chain problem with multiple-warehouse policy and budget consideration

This paper presents an efficient computation tool for a multiple-warehouse inventory replenishment lot-sizing problem with supplier selection, quantity discounts, and budget constraint. The cost model of the considered inventory replenishment issue is formulated as a mixed-integer nonlinear programming (MINLP) problem. With the proposed MINLP problem being an NP-hard problem, the MINLP cost model was optimized by using a novel global–local neighbor particle swarm optimization algorithm (TPAGLNPSO), inspired by two-phase self-adaptive inertia weight and improved constraint-handling technique. Apart from introducing the velocity index to switch the search scheme from exploration to exploitation, the constraint-handling technique was utilized to guide and guarantee the particle search toward undiscovered regions in the solution space. The stability analysis for the proposed novel TPAGLNPSO was also conducted using the stochastic process theorem. The corresponding parameter selection ranges were obtained based on the convergent conditions. Finally, eight industry cases were provided as examples to evaluate the performance of the proposed TPAGLNPSO method, after which a sensitivity analysis for the MINLP cost model was performed. The results demonstrate the competitive performance of the proposed TPAGLNPSO optimizer in terms of solution quality (with near-optimal results) than the optimal solutions obtained from Lingo software and computational effort (CPU time). Such findings can provide practitioners marginal insights into the effective management of the supply chain inventory replenishment problem.

Design and optimization of a TiO2/RGO-supported epoxy multilayer microwave absorber by the modified local best particle swarm optimization algorithm

Abstract Microwave absorbers have many applications in medical, industrial, and military devices. Polymeric composites including carbon-based filler can be used as lightweight absorbers with high electromagnetic (EM) wave absorption performance. Hence, multilayer microwave absorbers were designed using titanium dioxide (TiO2)/reduced graphene oxide (RGO)/epoxy nanocomposites with different weight percentages manufactured using refluxing and annealing methods. The characterization of nanocomposite indicated thin layers of TiO2/RGO as divided sheets in epoxy. The EM properties of the nanocomposites were examined using the Nicolson-Ross-Weir (NRW) detection method. The S-parameters were measured using PNA-N5222A Microwave Network Analyzer. The multilayer absorber software was designed based on the modified local best particle swarm optimization algorithm by MATLAB software, in which the material and thickness of layers were optimized with two cost functions in X-band frequencies. The first cost function seeks to reach the best absorption bandwidth, and the second cost function seeks to reach the maximum average return loss (RL) of the frequency range of 8.2–12.4 GHz. A maximum bandwidth with an RL of less than −12.81 dB was obtained with a thickness of 2.4 mm. A maximum average RL of −22.1 dB was obtained with a thickness of 2.6 mm. The maximum absorption peak was observed with a thickness of 2.5 mm with −62.82 dB at a frequency of 10.86 GHz.

Identification of Biomarkers for Arsenicosis Employing Multiple Kernel Learning Embedded Multiobjective Swarm Intelligence.

Arsenic is a carcinogen, and long-term exposure to it may result in the development of multi-organ disease. Understanding the underlying intricate molecular network of toxicity and carcinogenicity is crucial for identifying a small set of differentially expressed biomarker genes to predict the risk of the exposed population. In this paper, a multiple kernel learning (MKL) embedded multi-objective swarm intelligence technique has been proposed to identify the candidate biomarker genes from the transcriptomic profile of arsenicosis samples. To achieve the optimal classification accuracy along with the minimum number of genes, a multi-objective random spatial local best particle swarm optimization (MO-RSplbestPSO) has been utilized. The proposed MO-RSplbestPSO also guides the multiple kernel learning mechanism which provides data specific classification. The proposed computational framework has been applied to the developed whole genome DNA microarray prepared using blood samples collected from a specific arsenic exposed area of the Indian state of West Bengal. A set of twelve biomarker genes, with four novel genes, are successfully identified for the classification of exposure to arsenic and its subcategories, which can be used as future prognostic biomarkers for screening of arsenic exposed populations. Also, the biological significance of each gene is detailed to delineate the complex molecular networking and mode of toxicity.

Magnetic inversion modeling of subsurface geologic structures for mineral deposits mapping in southeastern Nigeria

Magnetic inversion techniques have been implemented to infer the extension and geometry of magnetic structures and also evaluate its influence on mineralization within Abakaliki and its environs, southeastern Nigeria. The modeling approach considers the techniques of three-dimensional (3D) magnetic data inversion, Euler deconvolution, analytic signal inversion, Enhanced Local Wavenumber (ELW) Technique and Particle Swarm Optimization (PSO) to estimate source parameters and compare results. Model solutions were interpreted to represent possible geologic units with varying trends, housing mineralization within the study region. Results from inversion computation over some active mine locations show subsurface bodies with magnetic susceptibilities >0.00188 SI. Model results also show structural sources with almost 5.5 km depth extension, stretching 18 km in the EW direction at Ngbo – Ekerigwe location. This could imply significant mineral deposits at the location. Inversion of both magnetic anomaly and analytical signal enabled derivation of the actual subsurface structures in the region, with most of the structures appearing as dykes with depths ranging from 0.2 – 1.8 km at most of the mining sites. Location and depths of some of the modeled intrusions have been corroborated with the active on-site mines. The delineation of mineralization structures by this study would guide systematic exploration in the region.

Multi-objective global and local Surrogate-Assisted optimization on polymer flooding

Oil production and polymer injection are two performance indicators of polymer flooding and are usually conflicting objectives. In order to obtain optimal trade-off solutions, this paper proposes a multi-objective global and local surrogate-assisted particle swarm optimization (MO-GLSPSO) method, which consists of alternative steps: global population prescreen and local population search.The global steps use generalized regression neural network (GRNN) to prescreen a better population, and the local steps use radial basis function (RBF) as proxy to search for the next generation. The global steps aim to reduce the chance of generations being trapped in local minima, and the local steps obtain the optimal solutions with a fast convergence rate. The rates (liquid production rate and water injection rate) and polymer injection concentration of wells are tuned to obtain a Pareto-front that maximizes cumulative oil production and minimizes cumulative polymer injection.The MO-GLSPSO method is tested using both synthetic and Brugge benchmark cases. The iterations generally improve the oil production or reduce polymer injection and are stabilized at a Pareto-front of the two objectives. Improved sweep efficiency and polymer utility are also observed in the optimal results. The proposed method is also compared with other two methods, multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), to examine the pros and cons. The results indicate that MO-GLSPSO has a better pareto-front than others.

Hybrid Algorithm for Large Scale in Electric Vehicle Routing and Scheduling Optimization

This study examines the optimal scheduling and routing of electric vehicles across multiple time periods to reduce battery consumption and commute times. The objective is to convey products to consumers using a fleet of electric vehicles as efficiently as feasible. This hybrid solution addresses this issue and combines the most advantageous aspects of the Local Search 2-Opt and Particle Swarm Optimization (PSO) metaheuristics. Local Search 2-Opt is used to enhance the quality of the original solutions, whereas PSO is used to search through all possible solutions and identify the best one. Considering constraints such as limited vehicle capacity, limited charging station availability, and limited time, the proposed method optimizes the coupling of consumers with electric vehicles. The evaluation results indicate that the hybrid algorithm accomplishes more significant energy savings, travel time savings, and overall solution quality than the alternatives currently considered to be state-of-the-art. This research's findings can be used to optimize EVRS operations and improve the administration of electric vehicle fleets.

A discrete differential evolution with local search particle swarm optimization to direct angle and aperture optimization in IMRT treatment planning problem

Intensity-modulated radiation therapy is a well-known technique for treating cancer patients worldwide. A treatment plan in this technique requires decision-making for three main problems: selection of beam angles, intensity map calculation, and leaf sequencing. Previous works investigated these problems sequentially. We present a new integrated framework for simultaneous decision-making of directions, intensities, and aperture shape, called direct angle and aperture optimization, and develop a mixed-integer nonlinear mathematical model for the problem. Due to the nonlinearity and the dimension of the problem, three efficient metaheuristics based on differential evolution (DE) called classic differential evolution (cDE), discrete differential evolution (dDE), and adaptive hybrid discrete differential evolution-particle swarm optimization (ahdDE-PSO) algorithms are designed to solve the problem. Parameters calibration is performed using the Taguchi design of experiments. The performance of the algorithms is evaluated by solving the problem for ten real cases of liver cancer disease from the TROTS data set. The performed ablation study and statistical analysis of computational results demonstrate that ahdDE-PSO is capable of finding high-quality treatment plans.

Adaptive Unsupervised-Shadow-Detection Approach for Remote-Sensing Image Based on Multichannel Features

Shadow detection is an essential research topic in the remote-sensing domain, as the presence of shadow causes the loss of ground-object information in real areas. It is hard to define specific threshold values for the identification of shadow areas with the existing unsupervised approaches due to the complexity of remote-sensing scenes. In this study, an adaptive unsupervised-shadow-detection method based on multichannel features is proposed, which can adaptively distinguish shadow in different scenes. First, new multichannel features were designed in the hue, saturation, and intensity color space, and the shadow properties of high hue, high saturation, and low intensity were considered to solve the insufficient feature-extraction problem of shadows. Then, a dynamic local adaptive particle swarm optimization was proposed to calculate the segmentation thresholds for shadows in an adaptive manner. Finally, experiments performed on the Aerial Imagery dataset for Shadow Detection (AISD) demonstrated the superior performance of the proposed approach in comparison with traditional unsupervised shadow-detection and state-of-the-art deep-learning methods. The experimental results show that the proposed approach can detect the shadow areas in remote-sensing images more accurately and efficiently, with the F index being 82.70% on the testing images. Thus, the proposed approach has better application potential in scenarios without a large number of labeled samples.

Hyperspectral Band Selections for Enhancing the Discrimination of Difficult Targets Using Local Band Index and Particle Swarm Optimization

Due to their similar color and material variability, some ground objects have similar characteristics and overlap in some bands. This leads to a drop in the classification accuracy of hyperspectral images. To address this problem, we simulated hyperspectral images of vegetation and objects with similar colors by mixed pixel calculation to test the classification performance of the dimensionality reduction method for samples with close spectra. In addition, we proposed a novel wavelength selection algorithm called the LBI-BPSO (Binary Particle Swarm Optimization with Local Band Index), which combines the information amount and inter-class separability. The novelty of this study is in its proposal of an improvement of IOIF using inter-class distance. Based on the calculation of the information content by the local band index, the inter-class distance was introduced to measure the inter-class separability of ground objects, and a reasonable fitness function is proposed. It can obtain the wavelength combination of two DR criteria, which considers the larger amount of information and better sample separability. The classification performance of the simulation dataset is verified by comparing LBI-BPSO with Partitioned Relief-F, IOIF (Improved Optimum Index Factor) and GA-BPSO (Particle Swarm Optimization with a Genetic Algorithm). Under the conditions that the signal-to-noise ratio is 1000, compared with IOIF, the OA of LBI-BPSO improved by 2.90%, the AA improved by 2.75%, and the Kappa coefficient improved by 3.91%. LBI-BPSO also showed the best results in the analysis of different abundances and signal-to noise-ratios. The results show that the new wavelength selection algorithm LBI-BPSO, which combines the amount of information and inter-class separability, is more effective than IOIF and GA-BPSO in classifying objects with similar colors and effectively improves the classification accuracy.

Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.

Diabetic retinopathy is a chronic condition that causes vision loss if not detected early. In the early stage, it can be diagnosed with the aid of exudates which are called lesions. However, it is arduous to detect the exudate lesion due to the availability of blood vessels and other distractions. To tackle these issues, we proposed a novel exudates classification from the fundus image known as hybrid convolutional neural network (CNN)-based binary local search optimizer-based particle swarm optimization algorithm. The proposed method from this paper exploits image augmentation to enlarge the fundus image to the required size without losing any features. The features from the resized fundus images are extracted as a feature vector and fed into the feed-forward CNN as the input. Henceforth, it classifies the exudates from the fundus image. Further, the hyperparameters are optimized to reduce the computational complexities by utilization of binary local search optimizer (BLSO) and particle swarm optimization (PSO). The experimental analysis is conducted on the public ROC and real-time ARA400 datasets and compared with the state-of-art works such as support vector machine classifiers, multi-modal/multi-scale, random forest, and CNN for the performance metrics. The classification accuracy is high for the proposed work, and thus, our proposed outperforms all the other approaches.

Particle Swarm Optimization Video Streaming Service in Vehicular Ad-Hoc Networks

Owing to the increasing capabilities of mobile devices and the development of mobile communication techniques in vehicular ad hoc networks (VANETs), mobile multimedia services have focused on supporting high Quality of Service (QoS) and Quality of Experience (QoE) for the subscribers in video streaming services. In VANETs, high-quality video streaming services aim to provide safety, as well as various infotainment applications to the subscribers. Owing to the dynamic topology and frequent connectivity changes in moving vehicles, video streaming services require elastic and continuous updates of vehicle information to present interactive real-time views of nontrivial scenarios on roads. To achieve high QoS and QoE of video streaming services in VANETs, this paper proposes a novel protocol named PSOstreaming that is based on a particle swarm optimization (PSO) which is one one of the mainstream and nature-inspired algorithms for swarm intelligence (SI). PSOstreaming calculates the PSQ score, which is the scoring method of the node in the topology in terms of the data communication capability measurement to analyze and optimize topology information in real-road circumstances. In addition, PSOstreaming utilizes a 3D vector mobility prediction algorithm for the mobility prediction method for the vehicles to address the characteristics of VANETs. In the topology, PSOstreaming defines Global PSO members and Local PSO members to disseminate video packets for video-streaming services to the service requester by computational equations. Experimental results indicate that PSOstreaming achieves high-quality video streaming services with a flexible response to dynamic topology changes and a high frame delivery ratio in terms of QoS and QoE.

Automatic Classification of Covid-19 Chest X-Ray Images Using Local Binary Pattern and Binary Particle Swarm Optimization for Feature Selection

Novel Coronavirus disease 2019 (COVID-19) is a type of pandemic viruses that cause respiratory tract infection in humans. The clinical imaging of Chest X-Ray (CXR) by Computer Aided Diagnosis (CAD) plays an important role to identify the patients who infected by COVID-19. The objective of this paper presents a Computer Aided Diagnosis (CAD) method for automatically classify 110 frontal CXR images of contagious people according to Normal and COVID-19 infection. The proposed method contains of four phases: image enhancement, feature extraction, feature selection and classification. Gaussian filter is performed to de-noise the images and Adaptive Histogram Equalization (AHE) for image enhancement in pre-processing step for better decision-making process. Local Binary Pattern (LBP) features set are extracted from the dataset. Binary Particle Swarm Optimization (BPSO) is considered to select the clinically relevant features and developing the robust model. The successive features are fed to Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The experimental results show that the system robustness in classification COVID-19 from Normal images with average accuracy 94.6%, sensitivity 96.2% and specificity 93%.

Electric vehicle charging and discharging scheduling strategy based on local search and competitive learning particle swarm optimization algorithm

It is great significance for environmental protection, energy conservation and emission reduction to replace fuel vehicles with EVs(electric vehicles).However, as a kind of random mobile load, large-scale integration into the power grid may lead to power quality problems such as line overload, line loss increase, voltage reduction and so on. In order to minimize the adverse effect of the disordered charging of EVs on the distribution grid, this paper takes the typical IEEE-33 node distribution system as the research object, a backward learning competitive particle swarm optimization (PSO) algorithm based on local search (SW-OBLCSO) is proposed. The SW-OBLCSO algorithm competitive learning and reverse learning mechanisms. In order to verify the performance of the algorithm, 4 common test functions are used, test functions compare the SW-OBLCSO algorithm with multiple optimization algorithms in different dimensions. The experimental results show that the proposed algorithm has outstanding performance in convergence speed and global search ability. This paper takes the minimum operation cost, the minimum environmental pollution, the minimum peak valley difference of load, the minimum node voltage offset rate, the minimum system grid loss and lowest charge cost as the optimization objectives; results shows that the proposed scheme can realize the transfer of charging load in time and space, so as to stabilize the load fluctuation of distribution grid, improve the operation quality of power grid, reduce the charging cost of users, and achieve the expected research objectives.