Population-based Optimization Method Research Articles

Performance improvement of PV systems during dynamic partial shading conditions using optimization algorithms

PV power plants encounter varying levels of irradiance, temperature fluctuations, and partial shading because of the differences in sunlight conditions. Partial shading can cause an increase in the power loss, leading to a reduction in efficiency. Maximum Power Point Tracking (MPPT) is of utmost importance in the functioning of photovoltaic (PV) systems for electricity generation because it is indispensable for maximizing power extraction from PV modules, thereby increasing the overall power output. In situations where partial shading is present, the utilization of MPPT algorithms to achieve maximum power output becomes complex because of the existence of multiple distinct peak power points, each having a unique local optimum. To overcome this issue, a method is proposed that uses Darts Game Optimization (DGO), a game-based optimization process, to efficiently determine and extract the maximum power from various local optimal peaks. A population-based optimization method known as the Darts Game Optimization algorithm exists. In this approach, the optimization process begins by creating a population of random players. Then, the algorithm iteratively updates and improves the population to search for the best player or solution. In this study, the DGO algorithm was applied to the MPPT process for voltage optimization in the PV procedure. The DC-DC converter is utilized to capture the maximum available power, and the findings demonstrate that the DGO algorithm efficiently identifies the global maximum, resulting in accelerated convergence, reduced settling time, and minimized power oscillation. Through simulations, the feasibility and effectiveness of the DGO centered MPPT approach was confirmed and compared with MPPT algorithms relying on perturb and observe (P&O) and Particle Swarm Optimization (PSO). The simulation results offer compelling evidence that the DGO algorithm, as proposed in this study, proficiently traces the global maximum, thereby substantiating its practicality and efficiency.

Dynamic Demand Modeling Incorporating Renewable Energy Sources Using a Population-Based Optimization Method

Due to the inclusion of distributed generation (DG) in microgrids (MGs), the accelerated growth in demand, and environmental concerns, suitable management and operational strategies are imperative. The utilization of wind and solar energy has rapidly increased in MGs. However, due to the uncertainties these systems present, accurately predicting energy generation remains challenging. This necessitates modeling the system’s random variables (such as renewable resource output and possibly load demand) using appropriate and feasible methods. The primary objective of this article is to determine the optimal setpoints for renewable energy sources (RES) and all elements involved in the MG, minimizing the total operation cost. The system comprises wind turbines (WT), photovoltaic panels (PV), energy storage systems (ESS), and electric vehicles (EVs). Weibull distribution and the Hottel and Liu Jordan equations are employed to determine the potential available capacity of wind and solar energy generation, respectively. ESS is utilized to enhance MG performance. For optimal management, a comprehensive mathematical model with practical constraints for each MG element is extracted. An efficient Population-Based Incremental Learning (PBIL) metaheuristic method is proposed to solve the optimization objective in an MG, demonstrating that this energy management system optimizes and effectively coordinates DG and ESS energy generation considering economic considerations. Finally, PBIL is compared with a commonly used model, Particle Swarm Optimization (PSO), across various scenarios, analyzing and evaluating their outcomes, showcasing a reduction in operation costs.

Software Testing Using Cuckoo Search Algorithm with Machine Learning Techniques

Software testing are any errors, flaws, bugs, mistakes, failures in a piece of software that might cause the programme to produce incorrect or unexpected results. Testing in software almost always increase both the time and money needed to finish a project. And finding bugs and fixing them is a laborious and expensive software process in and of itself. While it's unrealistic to expect to completely eradicate all testing from a project, their severity may be mitigated. It is possible to predict where bugs may appear in software using a method known as software defect prediction (SDP). The goal of each software development project should be to provide a bug-free product. Predicting where bugs may appear in code, often known as software defect prediction (SDP), is an important part of fixing software. Software of a high calibre should have few bugs. A software metric is a quantitative or qualitative evaluation of some aspect of the programme or its requirements. One of the more recent population-based algorithms, Cuckoo Search (CS) was inspired by the flight patterns of some cuckoo species as well as the Lévy flying patterns of other birds and fruit flies. The needs for international convergence are met by CS. KNN is a significant non-parameter supervised learning technique. This paper presents an overview of Stochastic Diffusion Search (SDS) in the form of a social metaphor to illustrate the processes by which SDS allots resources. The best-fit pattern identification and matching difficulties were addressed by SDS using a novel probabilistic method. As a multiagent population-based global search and optimization method, SDS is a distributed model of computing that makes use of interaction amongst basic agents. The behaviour of SDS is described by studying its resource allocation, convergence to global optimum, resilience, minimum convergence criterion, and linear time complexity within a rigorous mathematical framework, setting it apart from many nature-inspired search algorithms. This paper proposes a hybrid optimization strategy based on CS-SDS techniques. By using the global search strategy solution of the SDS algorithm, this hybridization idea aims to enhance the cuckoo bird's search strategy for the optimum host nest. To that end, the SDS method would be used to place the cuckoo egg in the most advantageous location. When compared to other classifiers, PC2's improved performance may be attributed to its higher recall values. When compared to the Naive Bayes and Radial Bias Neural Network classifiers, the KNN performs 7.64% and 2.20% better, respectively.

Multidisciplinary optimization of high aspect ratio composite wings with geometrical nonlinearity and aeroelastic tailoring

This study presents a systematic numerical approach for the design and optimization of high aspect ratio composite wings subjected to aerodynamic loads. The primary objective is to develop a multi-objective, multi-disciplinary optimization framework that considers aerostructural constraints, such as subsonic aeroelasticity and geometrical nonlinearity. The incorporation of anisotropic properties of composite materials is emphasized to construct lightweight aerospace structures. Aeroelastic tailoring, a technique leveraging these properties, is employed in the optimization process. The proposed methodology integrates three analysis tools, Finite Element software for structural behavior simulation, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses with tailoring capabilities, and Particle Swarm Optimization (PSO) as a population-based stochastic optimization method. This integration enables the development of a powerful numerical approach, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, for designing composite wings with optimized aeroelastic and structural performance. The proposed methodology has broad applicability in aerospace engineering, encompassing aircraft and unmanned aerial vehicles, offering significant potential to enhance their design and overall performance.

Analysis of one-shot device testing data under logistic-exponential lifetime distribution with an application to SEER gallbladder cancer data

In the literature, the reliability analysis of one-shot devices is found under accelerated life testing in the presence of various stress factors. The application of one-shot devices can be extended to the bio-medical field, where we often evidence that inflicted with a certain disease, survival time would be under different stress factors like environmental stress, co-morbidity, the severity of disease etc. This work is concerned with a one-shot device data analysis and applies it to SEER gallbladder cancer data. The two-parameter logistic-exponential distribution is applied as a lifetime distribution. For robust parameter estimation, weighted minimum density power divergence estimators (WMDPDE) is obtained along with the conventional maximum likelihood estimators (MLE). The asymptotic behaviour of the WMDPDE and the robust test statistic based on the density power divergence measure are also studied. The performances of estimators are evaluated through extensive simulation experiments. Later those developments are applied to SEER gallbladder cancer data. Citing the importance of knowing exactly when to inspect the one-shot devices put to the test, a search for optimum inspection times is performed. This optimization is designed to minimize a defined cost function which strikes a trade-off between the precision of the estimation and experimental cost. The search is accomplished through the population-based heuristic optimization method genetic algorithm.

Population-Based Hyperparameter Tuning With Multitask Collaboration.

Population-based optimization methods are widely used for hyperparameter (HP) tuning for a given specific task. In this work, we propose the population-based hyperparameter tuning with multitask collaboration (PHTMC), which is a general multitask collaborative framework with parallel and sequential phases for population-based HP tuning methods. In the parallel HP tuning phase, a shared population for all tasks is kept and the intertask relatedness is considered to both yield a better generalization ability and avoid data bias to a single task. In the sequential HP tuning phase, a surrogate model is built for each new-added task so that the metainformation from the existing tasks can be extracted and used to help the initialization for the new task. Experimental results show significant improvements in generalization abilities yielded by neural networks trained using the PHTMC and better performances achieved by multitask metalearning. Moreover, a visualization of the solution distribution and the autoencoder's reconstruction of both the PHTMC and a single-task population-based HP tuning method is compared to analyze the property with the multitask collaboration.

Using the Modified Artificial Bee Colony Algorithm to Find the Non-Archimedean Epsilon for Evaluating the Efficiency in DEA

The artificial bee colony algorithm is one of the population-based optimization methods inspired by the evolutionary principles of the social behavior of bees. On the other hand, one of the sub-fields of operations research science is data envelopment analysis. There are some difficulties in DEA models for selecting the appropriate numerical value for an infinitesimal non-Archimedean epsilon. So far, various methods have been proposed to solve this problem and choose the suitable non-Archimedean epsilon. In order to solve the problem, the artificial bee colony algorithm (ABC), and modification of the original ABC algorithm (MABC) are adopted and proposed in this paper. The impacts of our proposed algorithms on the suitable non-Archimedean epsilon by solving only one linear programming (LP), instead of n LP are investigated. Finally, the performance of the proposed algorithms is evaluated by comparing the solutions obtained from GAMS software based on the presented examples.

Great Wall Construction Algorithm: A novel meta-heuristic algorithm for engineer problems

In recent years, the optimization community has witnessed a surge in the popularity of population-based optimization methods. However, many of these methods suffer from various shortcomings, including unclear performance characteristics, incomplete validation, excessive reliance on metaphors, inadequate exploration and exploitation components, and compromised trade-offs between exploration and exploitation in real-world scenarios. As a result, users often find themselves needing to extensively modify and fine-tune these methods to achieve faster convergence, stable balance, and high-quality results. To shift the optimization community’s focus towards performance rather than metaphorical changes, we propose a general population-based optimization technique called the Great Wall Construction Algorithm (GWCA). This study presents GWCA as a simple yet robust method with competitive performance for efficiently solving constrained and unconstrained problems. GWCA draws inspiration from the competition and elimination mechanisms observed among workers during the construction of the ancient Great Wall. It introduces a mathematical model of the labor movement to simulate the algorithm’s dynamics. Unlike other methods that employ multiple models to generate new solutions, GWCA randomly assigns a single predefined motion model to each worker in every iteration. This unique approach showcases GWCA’s dynamic nature, simple structure, high convergence performance, and ability to deliver satisfactory solution quality, thus outperforming existing optimization methods in terms of efficiency. To validate GWCA, we conduct extensive comparisons with popular and advanced algorithms on the IEEE CEC 2017 benchmark suite across different dimensions (D = 10, 30, 50, 100). Additionally, GWCA is applied to solve 16 constrained engineering problems and 6 NP-Hard problems, demonstrating its applicability in handling constrained and complex nonlinear problems. Finally, we compare GWCA’s optimized solutions with those obtained from 33 advanced meta-heuristic algorithms, including the winner of CEC 2017. The results confirm the effectiveness of the proposed optimizer in solving a wide range of single-objective problems, surpassing popular base optimizers, advanced variants of existing methods, and several CEC winners. We present GWCA as an open-source population-based method that can serve as a standard optimization tool across various domains of artificial intelligence and machine learning. It exhibits a range of exploratory and exploitative features, offering high performance and optimization capabilities. The method is highly flexible, scalable, and can be further extended in terms of structure and application to accommodate diverse forms of optimization scenarios. https://github.com/guangian/Great-Wall-Construction-Algorithm-a-novel-meta-heuristic-algorithm-for-global-optimization.

Opposition-based Laplacian distribution with Prairie Dog Optimization method for industrial engineering design problems

Prairie Dog Optimization is a population-based optimization method that uses the behavior of prairie dogs to find the optimal solution. This paper proposes a novel optimization method, called the Opposition-based Laplacian Distribution with Prairie Dog Optimization (OPLD-PDO), for solving industrial engineering design problems. The OPLD-PDO method combines the concepts of opposition-based Laplacian distribution and Prairie Dog Optimization to find near-optimal solutions. This causes faster convergence to the optimal solution and reduces the chances of getting stuck in a local minimum. The OPLD-PDO method was tested on several benchmark problems to validate its performance. The results were compared with other methods, and the OPLD-PDO method was superior regarding solution quality. The results of this study demonstrate the potential of the OPLD-PDO method as a useful tool for solving industrial engineering design problems and photovoltaic (PV) solar problems.

Parameter Space Exploration of Cellular Mechanical Metamaterials Using Genetic Algorithms

Cellular materials widely exist in natural biologic systems such as honeycombs, bones, and woods. With advances in additive manufacturing, research on cellular metamaterials is emerging due to their unique mechanical performance. However, the design of on-demand cellular metamaterials usually requires solving a challenging inverse design problem for exploring complex structure–property relations of microstructured representative volume elements (RVEs) in the design domain. Here, we propose an experience-free and systematic methodology for exploring a parametrized system for microstructures of cellular mechanical metamaterials using a multiobjective genetic algorithm (GA). Globally, by considering the importance of the initial population selection for a population-based heuristic optimization method, we study the impact of the populations initialized by the different sampling methods on the optimal solutions. Locally, we develop our method by using a micro-GA with a new searching strategy, which requires the standard genetic algorithm to be conditionally run for a sufficient number of times with a small population size during the global searching process. We have applied our method to explore optimal solutions for applications mapped on two different parameter spaces of the cellular mechanical metamaterials with periodic and nonperiodic RVEs effectively and accurately.

Future Trails for Integer Programming and Relations to Artificial Intelligence

Aims: A review of methods and approaches for solving linear integer problems is presented in this work. These problems are classified as NP-hard optimization algorithms in artificial intelligence.
 Study Design: we have used the Google scholar to collect the data resources from past 5 years to analysis the techniques and methods used in different algorithms in artificial intelligence.
 Methodology: Exact optimum solution for this class of challenges also need use of substantial computer resources. The current direction in which several researcher focuses their efforts to effectively address numerous difficult practical issues is the creation of efficient hybrid techniques that combine in an appropriate way the finest elements of multiple methods (precise or estimated). The approximation algorithms' core heuristic techniques might be classified as constructive algorithms and local-improvement algorithms.
 Results: We examined three artificial intelligence algorithms utilizing the linear integer programming approach. Algorithm based on population It has also been demonstrated that a population of a critical size is necessary for a population-based optimization method to be effective. The genetic algorithm is shown next. The goal value associated with this solution may be utilized to effectively reduce the search tree in bound and branch type integer programming methods. Finally, we analyze the particle swarm optimization (PSO) approach, which demonstrates that In most cases, PSO outperforms the Branch and Bound method in solving such issues quickly. 
 Conclusion: In actuality, integer optimization issues describe a wide spectrum of real-world difficulties. Their population and size are constantly growing. Although while accurate methods for integer issues have substantially improved in recent years, their long runtimes and memory needs make them unsuitable for actual medium and large-scale applications.

An adaptive position-guided gravitational search algorithm for function optimization and image threshold segmentation

Gravitational search algorithm is a population-based optimization method. To address its low search performance and premature convergence, a novel variant called adaptive position-guided gravitational search algorithm is proposed. It utilizes the best, worst and other particles’ position information to adaptively determine the Kbest particles which provide a good movement direction. The gravitational force is reinforced by Kbest particles and new constructed Dbest particles to improve the exploration and exploitation abilities. Various particles’ position information jointly provide the effective search guideline and accelerate the convergence rate. Validations are conducted to firstly discuss the parameters and strategies of the proposed algorithm. Then, compared with several state-of-the-art gravitational search algorithm variants on CEC2017 benchmark functions, the proposed algorithm proves its superiority. Finally, the proposed algorithm exhibits the good segmentation effect on image threshold segmentation problems.

A Comprehensive Study of Local, Global, and Combined Optimization Methods on Synthetic Seismic Refraction and Direct Current Resistivity Data

Most geophysical inversions face the problem of non-uniqueness, which poses a challenge in the mapping and delineation of the subsurface anomalies. To tackle this challenge, a combined local and global optimization approach is considered for jointly inverting two-dimensional direct current resistivity (DCR) and seismic refraction (SR) data that aim to estimate the corresponding physical model parameters. In this combined approach, the output of the local optimization method is used to determine the search space and tuning parameters for the global optimization algorithm. The multi-objective genetic algorithm (non-dominated sorting genetic algorithm) was utilized to jointly optimize the objective functions of two different methods. Because the genetic algorithm is a population-based optimization method, it requires numerous forward calculations. To deal with the expected high computational cost associated with this approach, parallel computing was utilized for the forward function evaluations to reduce the run time of the entire process. The proposed approach was tested using synthetic two-dimensional resistivity and velocity models that had three different types of anomalies (dyke, positive, and combined positive and negative). The results showed an improvement in the anomaly delineation in the output of the combined local and global optimization method compared with the local optimization method. Additionally, similar synthetic models were tested using only the single objective global optimization algorithm (conventional global optimization), which showed promising anomaly delineation.

Improved spherical search with local distribution induced self-adaptation for hard non-convex optimization with and without constraints

Most metaheuristic optimizers rely heavily on precisely setting their control parameters and search operators to perform well. Considering the complexity of real-world problems, it is always preferable to adjust control parameter values automatically rather than clamping them to a fixed value. In recent years, Spherical Search (SS) has emerged as a population-based stochastic optimization method that exploits the concepts of random projection matrices in linear algebra. As a result of the success of SS in solving non-convex, real-parameter optimization problems of various complexity, we have significantly extended SS in this paper by introducing a set of new algorithms, collectively known as Self Adaptive Spherical Search (SASS). Our proposal aims to enhance the performance of SS by using different projection matrix schemes in conjunction with improved search-direction calculations and an adaptive modification of parameter values. In our proposed adaptation scheme, parameters are modified to relevant values by applying a self-adaptive process that does not rely upon prior knowledge of the correlation between the parameter values and characteristics of the problem space. Consequently, we may apply the algorithms to bound and nonlinearly constrained optimization problems. For the benchmark suites derived from the most recent IEEE Congress on Evolutionary Computation (CEC) competitions, simulation results indicate that the SASS family of algorithms performs better than or is comparable to state-of-the-art algorithms from the other paradigms concerning robustness and convergence.

Efficient bending and lifting patterns in snake locomotion

We optimize three-dimensional snake kinematics for locomotor efficiency. We assume a general space-curve representation of the snake backbone with small-to-moderate lifting off the ground and negligible body inertia. The cost of locomotion includes work against friction and internal viscous dissipation. When restricted to planar kinematics, our population-based optimization method finds the same types of optima as a previous Newton-based method. With lifting, a few types of optimal motions prevail. We have an s-shaped body with alternating lifting of the middle and ends at small-to-moderate transverse friction. With large transverse friction, curling and sliding motions are typical at small viscous dissipation, replaced by large-amplitude bending at large viscous dissipation. With small viscous dissipation, we find local optima that resemble sidewinding motions across friction coefficient space. They are always suboptimal to alternating lifting motions, with average input power 10–100% higher.

An operator pre-selection strategy for multiobjective evolutionary algorithm based on decomposition

Evolutionary algorithms (EAs) are a population-based optimization method that adopts survival-of-the-fittest rules. The performance of EAs can be greatly affected by offspring quality. Many researchers hope to adjust the parameters of evolutionary operators to improve offspring quality, but most current methods are regulated by the feedback model, which does not recognize offspring quality. Instead, it adjusts an operator’s search structure dynamically based on its historical performance. In fact, there is a co-evolutionary effect between different operators. An operator will not always produce offspring with the best quality, so the feedback model only selects the best operator in history to generate offspring, which ignores the capacity for co-evolution. In this paper, we propose an operator pre-selection strategy (OPS). First, to ensure operators’ capacity for co-evolution, we construct an operator pool using five operators. Second, we select the best offspring based on the classification and feedback models. Finally, we evaluate the offspring and include it in the evolution. Compared with the traditional feedback model, OPS directly identifies higher-quality offspring. Even if the historical performance of an operator is poor, as long as it can generate excellent offspring, it is selected. Experimental results demonstrate the effectiveness of OPS.

Stochastic stability analysis of particle swarm optimization with pseudo random number assignment strategy

Particle swarm optimization (PSO) is a population-based optimization method and has been successfully applied to solve many real-world problems. This method belongs to the stochastic optimization method and is mainly driven by two random streams utilized in the stochastic search mechanism, namely, individual (cognition) and social randomness effects. To our best knowledge, no research work has been conducted about the manipulation of the random stream assignment for stochastic search mechanism in the PSO algorithm. In this work, the influences of controlling randomness in the searching scheme of PSO is studied by introducing different pseudo random number (PRN) assignment strategies. The order-1 and order-2 stability analyses for particle dynamics under different PRN assignment strategies are also conducted to understand the influences. Stability analysis is carried out using the stochastic process theory. Our results show that the correlation caused by PRN has no effect on the unbiasedness of the expectation of particle position, but it would reduce or increase the variance of particle dynamics. Second, the convergent conditions of the PSO system under different PRN assignment strategies and the corresponding parameter selection ranges are provided. Finally, an empirical analysis via experimental simulations evaluated by six common swarm diversity measures, eight benchmark test functions, and two parameter tuples is presented.

Integration of distributed generations by coordinating electric vehicle charging station in medium voltage network by Swarm intelligence

PurposeNotable energy losses and voltage deviation issues in low-voltage radial distribution systems are a major concern for power planners and utility companies because of the integration of electric vehicles (EVs). Electric vehicle charging stations (EVCSs) are the key components in the network where the EVs are equipped to energize their battery. The purpose of this paper is coordinating the EVCS and distributed generation (DG) so as to place them optimally using swarm-based elephant herding optimization techniques by considering energy losses, voltage sensitivity and branch current as key indices. The placement and sizing of the EVCS and DG were found in steps.Design/methodology/approachThe IEEE 33-bus test feeder and 52-bus Indian practical radial networks were used as the test system for the network characteristic analysis. To enhance the system performance, the radial network is divided into zones for the placement of charging stations and dispersed generation units. Balanced coordination is discussed with three defined situations for the EVCS and DG.FindingsThe proposed analysis shows that DG collaboration with EVCS with suitable size and location in the network improves the performance in terms of stability and losses.Research limitations/implicationsStability and loss indices are handled with equal weight factor to find the best solution.Social implicationsThe proposed method is coordinating EVCS and DG in the existing system; the EV integration in the low-voltage side can be incorporated suitably. So, it has societal impact.Originality/valueIn this study, the proposed method shows improved results in terms EVCS and DG integration in the system with minimum losses and voltage sensitivity. The results have been compared with another population-based particle swarm optimization method (PSO). There is an improvement of 18% in terms of total power losses and 9% better result in minimum node voltage as compared to the PSO technique. Also, there is an enhancement of 33% in the defined voltage stability index which shows the proficiency of the proposed analysis.

Sensibility Analysis with Genetic Algorithm to Allocate Distributed Generation and Capacitor Banks in Unbalanced Distribution Systems

In the last years, many authors have dedicated themselves to solving the problem of distributed resources allocation in Distribution Systems (DS). The optimal siting and sizing of such equipment can improve the efficiency of the provided service, promoting technical and economic gains. Several papers use artificial intelligence algorithms and methods based on population evolutions to achieve this goal, due to the increasing complexity of the simulated systems and the highly combinatorial characteristics of this problem. Reducing the search space and/or setting the initial population are critical factors for improving the performance of population-based optimization methods. In the literature, the adjustment of the initial population does not receive the attention it should, especially when dealing with three-phase large-scale unbalanced systems – when computational time becomes primary adversity. The objective of this paper is to propose a novel approach for DS optimization, using Genetic Algorithms (GA) to map which areas are more sensitive to equipment allocation to reduce losses. In this work, capacitor banks and distributed photovoltaic (PV) generation are considered. The method adopted to carry out the allocation considers proper modeling of unbalanced DS through multiphase Optimal Power Flow frameworks. Its development takes advantage of the probabilistic nature of the GA to perform a sensitivity analysis to determine the positions that may reduce the losses in a daily planning horizon. For this, the execution of GA is done sequentially, under different conditions of generation and load, selected at random, setting in optimization a 24-hour load and PV generation curves that take into consideration the uncertainties, representing each hour of the day individually for better detail of the planning horizon. It was seen that the best positions are always concentrated in specific regions, called Optimal Allocation Zones (OAZ), which may favor the planning of Utilities. Furthermore, the OAZ method can be used to define an initial population of evolutionary algorithms, thus achieving a reduction in computational time and a better quality of solutions, which is a major contribution to literature. Through this approach, the computational time can be reduced up to 60% in large-scale systems. Simulations were carried out on four unbalanced distribution test-feeders: IEEE 4, IEEE 13, IEEE 37, and IEEE 123 Node Test Feeders.

Classification and Optimization of Basketball Players' Training Effect Based on Particle Swarm Optimization.

Since the professionalization of basketball in China, the number of teenagers participating in basketball training has gradually increased, which has promoted the improvement of basketball level in China. Teenagers ‘love' for basketball further promotes the improvement of basketball level in China. However, the reality of basketball in China still lags far behind that of developed basketball countries, in which backward training is an important aspect. This paper mainly makes a comprehensive overview of the training effect and classification of basketball players through particle swarm optimization, objectively evaluates the training effect of physical fitness, and proposes corresponding optimization measures, aiming at the scientific optimization of physical training for basketball players in China. In order to rationally arrange the training methods, control the training process, and make the training scientific, the effectiveness of the particle swarm optimization algorithm for the classification of basketball players' training effects is analyzed, and a new population-based optimization method is proposed. The experimental results verify the superiority of the particle swarm optimization algorithm. It is an inevitable choice to enhance the physical strength level of basketball reserve strength by using appropriate methods to train basketball players.