• Proposed a Novel Control Strategy of STATCOM with Two PID and Cascaded PID and FOPID Tuned by Combined GA-PSO Algorithm. • Conducted a comparative analysis with conventional controllers, evaluating IAE. • Validated the proposed controller's robustness and efficiency in maintaining stability under uncertainties. Voltage stability is a critical factor in the reliable operation of electrical power systems. The Static Synchronous Compensator (STATCOM), a member of the shunt-based FACTS (Flexible AC Transmission Systems) family is widely used as a voltage regulator to enhance grid stability. This study presents a novel STATCOM control strategy optimized using combined evolutionary algorithms. The control design integrates both conventional PID and Fractional Order PID (FOPID) controllers. For tuning the controller parameters, three optimization techniques are employed: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a hybrid GA-PSO method. Simulation and performance analysis are performed using MATLAB/Simulink and minimizing the Integral of Squared Error (ISE) of the voltage signal. Robustness is validated through simulations under various random grid parameter variations. The results demonstrate that the proposed GA-PSO-optimized PID-FOPID control strategy significantly improves voltage stability in the electrical grid.

Optimization of FTIR-PLS models for adulteration detection in sesame oil: a comparative study of genetic algorithm, particle swarm optimization, and a hybrid GA-PSO approach.

The present work investigates the modeling and parameter identification of solar photovoltaic systems using a hybrid optimization framework that integrates Genetic Algorithm and Particle Swarm Optimization methods. Solar photovoltaic systems are critical in the search for renewable energy sources, and their performances are highly dependent on environmental conditions like temperature, irradiance, and partial shading. The development of an accurate model is vital to ensure the efficient design and optimal performance of such systems. Conventional techniques of estimating parameters are not effective in handling nonlinearities and sensitivity associated with PV systems. The hybrid GA-PSO algorithm combines the global search capability of the Genetic Algorithm with fast convergence properties of Particle Swarm Optimization to conduct an efficient optimization of key parameters of the PV system, including photocurrent, series resistance, shunt resistance, and the diode ideality factor. The key focus of this paper was aimed at the enhancement of accuracy in the estimation of solar PV systems' parameters under varying environmental conditions, thus leading to better PV performance and efficiency. The research methodology involved simulating the solar PV system using MATLAB, optimizing key parameters using a hybrid GA-PSO algorithm, and model validation with experimental data. Optimized parameters are further utilized to develop current-voltage (I-V) and power-voltage (P-V) characteristics for different conditions of irradiance and temperature.

To address the issue of traditional Particle Swarm Optimization (PSO) being prone to local optima and insufficient global search capability in sparse phased array optimization, a hybrid optimization algorithm integrating PSO with a Genetic Algorithm (GA) is proposed. Within the PSO framework, the proposed algorithm incorporates the adaptive crossover and mutation operations of the GA to enhance population diversity. It combines an adaptive weighting factor and a constriction factor to balance global exploration and local exploitation capabilities. Furthermore, a density-weighted method is employed to generate a high-quality initial population, thereby accelerating convergence. The proposed algorithm is applied to an 8 × 8 planar sparse array. On the E-plane (φ = 0°) and H-plane (φ = 90°), simulation results indicate that the achieved normalized maximum sidelobe level is −23.14 dB, which is significantly superior to those obtained by standalone PSO and GA. Based on these simulation results, microstrip patch antennas are introduced for array constitution and analysis. Full-wave electromagnetic simulation proves that the proposed sparse array has the ability of wide-angle scanning and low sidelobe. Our work demonstrates that the PSO-GA hybrid algorithm effectively enhances search capability and convergence performance, providing a reliable solution for sparse array design.

Optimising PID controllers in complex environments requires algorithms that are efficient, adaptive and capable of learning. This research presents a Reinforcement-Driven Modified PSOGA (MPSOGA) for PID controller optimisation. The algorithm embeds Q-learning into the PSO-GA framework, enabling real-time adaptation of search strategies and improving the balance between exploration and exploitation. This reinforcement-driven mechanism enhances convergence reliability and overall solution quality. Simulation studies on benchmark PID control tasks demonstrate that MPSOGA achieves faster convergence, reduced overshoot and improved transient stability compared with PSO, GA, GWO, PSOGA and mJS. Additional evaluations on standard optimisation benchmarks indicate robustness across diverse problem settings. The results highlight the potential of MPSOGA as a practical and adaptable approach to control optimisation, contributing to ongoing research on reinforcement-driven metaheuristics.

This research addresses the issues of low efficiency, high error rates in traditional manual railway brake shoe replacement, and the mismatch between existing automated equipment and actual needs due to insufficient technical adaptability. It designs an intelligent management and control system integrating NFC technology and intelligent optimization algorithms, adopting a hierarchical architecture (perception, transmission, decision-making layers). NFC realizes automatic data acquisition; SVM and GAM models achieve data matching and traceability; a hybrid GA-PSO algorithm optimizes scheduling. Experimental verification shows the system significantly improves recognition accuracy, scheduling efficiency, and anomaly response speed. Innovations include an NFC-based full-link data acquisition pathway, an SVM-KNN integrated model for accurate data alignment, and a GA-PSO hybrid mechanism for optimal resource allocation. It shifts from manual to algorithm-driven operations, providing a promotable paradigm for digital transformation of railway operation and maintenance.

The Internet of Things (IoT) plays an important role in the development of smart cities. IoT forms a large network, and optimal controller placement plays a crucial role in ensuring network performance and resilience. This paper proposes a hybrid optimization approach that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to strategically place controllers. Kaunas (Lithuania) was selected as a real-world smart city model. A large-scale Narrowband Internet of Things (NB-IoT) network with 2000 nodes was simulated, and 10 controllers were optimally placed in the network to minimize latency, balance load, enhance energy efficiency, and redundancy. The performance of the proposed hybrid GA-PSO algorithm was compared with random and K-Means clustering placements under three scenarios: normal operation, node failures, and traffic spikes. Simulation results demonstrate that the hybrid approach outperforms the other two methods in terms of load balancing, packet loss, energy efficiency, scalability, and redundancy. These findings highlight the robustness and effectiveness of the proposed hybrid algorithm in optimizing controller placement for smart city environments.

Dual carbon goals have promoted the development of clean energy power generation in power system constructions, and photovoltaic power has become one of the key contents of power generation in new-type power systems. Due to the influence of light, temperature, load change and other factors, the traditional grid-connected inverter control method had the disadvantages of slow adaptive dynamic effect and poor stability, and the grid current with high harmonic content brought power quality problems to single-phase photovoltaic grid-connected inverter systems. Considering the harmonic content of the grid current and the current error integration as the objective function, the particle swarm was improved by the idea of crossover and mutation in genetic algorithms, and the adaptive weight improved PSO algorithm was applied to the dual-closed-loop inverter control strategy, which could update the particles’ weights with the iterations and fitness values. It also accelerated the global tracking speed of PSO algorithm optimization and realized the elaborate parameters setting of quasi-PR controller. Meanwhile, the stability and dynamic response capability of the grid-connected inverter has also been improved. MATLAB/Simulink simulation results showed that the quasi-PR Grid-Connected inverter based on improved GA-PSO algorithm had lower harmonic content of the grid-connected current under the working conditions of load change and DC voltage fluctuation, which enhanced the adaptive degree of the grid-connected inverter.

A sparse antenna array of subarrays can effectively reduce the digital channels of array antennas, system complexities, and hardware cost while simultaneously increasing the antenna aperture. In this study, a new optimal design is proposed for a sparse antenna array of subarrays in the full-phased multiple input multiple output (FPMIMO) operation mode based on genetic algorithm–particle swarm optimization (GA–PSO) and ambiguity functions. The proposed algorithm can adaptively adjust the number of optimization iterations for yielding the optimization results of the PSO algorithm and GA to ensure the global optimization performance of algorithms and combine ambiguity functions to determine the final optimized sparse antenna array of subarrays. The effectiveness of the proposed algorithm is verified via simulation tests.

To enhance the efficiency and precision of microfluidic biochip testing, this study proposes a hybrid optimization model integrating genetic algorithm (GA) and particle swarm optimization (PSO) with a dynamic priority strategy. Traditional methods, such as the ant colony algorithm and Euler loop method, often suffer from slow convergence or local optima in complex path optimization scenarios. By combining the global exploration capability of GA and the local exploitation strength of PSO, the proposed GA-PSO algorithm dynamically adjusts search priorities to minimize interference between experimental and test droplets. This approach optimizes test paths for both offline and online testing modes. Experiments on chips ranging from 7 × 7 to 15 × 15 arrays demonstrate significant improvements: the 15 × 15 chip achieves a shortest path length of 442 in both modes, reducing iterations by 60.9% for offline and 16.9% for online testing compared to standalone GA or PSO. Compared to the ant colony algorithm and Euler loop method, the proposed method shortens offline test paths by 4.91% and 5.56%, respectively, and online test paths by 8.98% and 9.80%. Key contributions include (1) a novel hybrid algorithm balancing global and local search, (2) a dynamic priority strategy mitigating droplet interference, and (3) a universal framework applicable to diverse chip specifications. These advancements offer practical guidance for real-time detection and batch processing in biomedical engineering, significantly improving testing efficiency.

Abstract Aiming at the problems such as limited freedom of movement and rigid path of traditional casting sorting platform, the pose adjustment accuracy of casting is insufficient, this paper proposes an innovative structure of multi-dimensional vibration pose adjustment platform based on substance-field analysis, and proposes an improved particle swarm optimization (IMPSO) control algorithm based on adaptive dynamic weights. In terms of mechanical structure, the multi-dimensional vibration excitation module is introduced and the innovative design of the toggle mechanism is proposed, which breaks through the traditional single path sorting mode and realizes the casting position adjustment; In terms of control strategy, the PSO algorithm is optimized and improved by introducing the adaptive interference force and dynamically adjusting the weights according to the trend of the change of the value of the particle adaptation degree, the IMPSO, PSO, genetic particle swarm optimization algorithm (GA-PSO), and chaotic particle swarm optimization algorithm (CPSO) algorithms were tested on four test functions: Sphere, Griebank, Rastrigin, and Rosenbrock, and the results showed that the IMPSO algorithm has superiority in optimal solution, convergence accuracy, and algorithm stability; In order to achieve precise exploration of the optimal pose of castings, simulation experiments were conducted on IMPSO, PSO, GA-PSO, and CPSO algorithms using the difference in casting position and orientation as the fitness function. The experimental results show that the IMPSO algorithm improves the iterative convergence speed by 26%, 10%, and 27% compared to PSO algorithm, GA-PSO algorithm, and CPSO algorithm, respectively, verifying its effectiveness in practical scenarios A prototype experiment was conducted, and the experimental results showed that the maximum deviation of the position and attitude adjustment of the casting by the adjustment platform was controlled within 0.34785 cm and 0.24352°, respectively, with relatively small errors. This research offers theoretical methods and technical references for high—precision flexible sorting in complex industrial scenarios.

In this paper, an optimization strategy for voltage level planning using intelligent algorithms is constructed to improve the power supply capacity (PSC) and overall performance of the power grid. The Laida criterion is used to clear abnormal data, and the weighted average method is used to supplement the missing data; the adaptive Particle Swarm optimization algorithm (APSO) is used to construct a voltage-level collaborative planning model, through flexible adjustment of inertial weights and acceleration factors, to achieve a balance between global search and local fine-tuning; by combining particle swarm optimization (PSO) and Genetic algorithm (Genetic Algorithm, GA), a hybrid algorithm (PSO-GA) is formed, effectively avoiding the dilemma of local optimization by introducing random self-feedback variation and high-frequency cross-operation. The results show that the energy utilization rate and transmission efficiency of the APSO algorithm have increased to 94.3% and 92.8%, respectively; the PSC of the PSO-GA algorithm has increased by 20.61% and 22.44% under low-load and medium-load conditions, respectively. Both algorithms effectively solve the voltage planning challenges, significantly reduce energy consumption, enhance the synergy of the voltage level, and improve the maximum PSC.

Summary Under China’s 100-million-ton annual oil and gas production target, optimizing new well investments is critical for stabilizing output and sustaining national energy security, yet existing models face two key challenges: (1) Single-objective frameworks fail to balance economic viability, geopolitical risks, and environmental sustainability, and dynamic political environments in overseas regions lack embedded risk-benefit analysis for strategic planning; (2) high-dimensional feasibility spaces (involving over a thousand well combinations) render traditional methods computationally inefficient. This paper focuses on evaluating the effectiveness of a single well in oil and gas production outside mainland China. This study pioneers the integration of the support vector machine (SVM) and a hybrid genetic algorithm (GA) with particle swarm optimization (PSO) into a multiobjective framework tailored for overseas oil and gas investment decisions, addressing the critical challenge of balancing economic viability, geopolitical risks, and environmental sustainability. New wells are essential for meeting this stable production goal, which significantly impacts overall oil and gas output. Unlike conventional single-objective models, our approach simultaneously maximizes economic returns, minimizes risks from host nation policies and carbon emissions, and optimizes portfolio diversification by balancing Chinese equity stakes and regional preferences. The SVM method utilized in this paper effectively transforms multiobjective optimization into single-objective optimization through a weighted approach. The novel mutation mechanism, inspired by PSO’s position displacement strategy, enables dual-phase search dynamics: initial global exploration via swarm intelligence to identify high-potential solution regions, followed by GA-driven local refinement to converge on optimal portfolios. The efficacy of the constructed GA-PSO algorithm is subsequently validated through a case study. In comparison with traditional methods, this algorithm enhances computational performance, yields superior economic benefits, optimizes the production of single-well investment portfolio, and mitigates investment risks. By generating actionable frontiers, the framework supports annual capital allocation strategies that reconcile conflicting objectives—such as short-term profitability vs. long-term decarbonization, and simulate outcomes under varying geological and geopolitical conditions. Beyond technical innovation, it establishes a replicable template for aligning economic efficiency, risk mitigation, and environmental compliance in politically dynamic regions, directly informing national energy security policies while demonstrating how hybrid metaheuristics advance global energy projects toward sustainability amid evolving geopolitical complexities. Beyond technical optimization, the framework provides a replicable template for balancing economic efficiency, risk mitigation, and environmental compliance in politically dynamic regions, demonstrating how advanced computational tools can enhance strategic planning in global energy projects while advancing the sector’s transition toward sustainable practices.

Aiming to address the problem of accuracy degradation in Delta robots caused by machining accuracy, assembly precision, etc., this paper corrects the robot’s driving angles to achieve error compensation and designs a compensation algorithm based on particle swarm optimization (PSO) and BP neural network. In terms of algorithm improvement, the inertia weight and learning factors of the PSO algorithm are optimized to effectively enhance the global search ability and convergence performance of the algorithm. Additionally, the core mechanisms of genetic algorithms, including selection, crossover, and mutation operations, are introduced to improve algorithm diversity, ultimately proposing an improved PSO-GA-BP error compensation algorithm. This algorithm uses the improved PSO-GA algorithm to optimize the optimal correction angles and trains the BP network with the optimized dataset to achieve predictive compensation for other points. The simulation results show that the comprehensive error of the robot after compensation by this algorithm is reduced by 83.8%, verifying its effectiveness in positioning accuracy compensation and providing a new method for the accuracy optimization of parallel robots.

The main objective of the present study is to predict the optimal availability of biscuit manufacturing plants using genetic algorithm (GA), particle swarm optimization (PSO), and Hybrid GA-PSO algorithms. The biscuit manufacturing system (BMS) is a complex industrial entity having six components configured in series. For availability investigation, a novel stochastic framework is developed for BMS. Under various operating situations, the dynamic behaviour of the plant is captured by this framework. Markov birth–death process is used to model the system's behaviour. As well, the most crucial subsystem is identified by employing a thorough reliability, availability, maintainability, and dependability (RAMD) examination. By considering component failure and repair rates as exponentially distributed, the performance of the BMS evaluated under a set of assumptions. It is revealed that the hybrid GA-PSO provides the optimal availability 0.99950181 at population size 50 after 5 iterations and it outperforms over GA and PSO. The robustness of the algorithm shown with the help of descriptive summary statistics as well as with non-parametric statistical tests. It is observed that the convergence rate of hybrid GA-PSO is very fast in comparison to traditional GA and PSO. The same methodology may be opted for performance evaluation of similar kinds of other manufacturing industries.

Compressed Sensing(CS), Genetic Algorithm(GA), and Particle Swarm Optimization algorithm(PSO) are widely used in the study of neutron energy spectrum. CS run fast but with low accuracy, and GA and PSO have strong global search capabilities but run slowly. Therefore, this paper proposes the unfolding of the neutron energy spectrum of the 241Am-Be neutron source by combining CS with Improved Particle Swarm Based on Genetic Algorithm(GA-PSO). The result of CS is used as the initialized particle population of GA-PSO to accelerate the convergence of the algorithm. The results show that the neutron energy spectrum obtained by solving the spectrum of the method proposed in this paper has the smallest root mean square error(RMSE) with a value of 0.055, and the spectral similarity is very close to 1. RMSE is improved by about 11% compared to the CS, and the running speed is shortened from roughly 4 hours to about 8 seconds compared to the GA-PSO.

A digital twin for a wind turbine gearbox (WTG-DT) is essential for advancing wind farm intelligence and improving the efficiency of wind turbine operation. This study addresses key limitations of existing condition monitoring systems, such as low accuracy and slow parameter updates, by proposing a long short-term memory (LSTM) network to intelligently calibrate model parameters. This ensures the real-time operation and maintenance of wind turbines. A high-fidelity dynamic model is developed and validated by performing frequency analysis of vibration signals collected through the condition monitoring system (CMS), with wind speed and load data from the supervisory control and data acquisition (SCADA) system as inputs. To simplify the complex finite element analysis process, a parameter sensitivity analysis is conducted, and a GA-PSO optimization algorithm, based on genetic algorithms, is applied. These methods generate sufficient training data for constructing an LSTM-based predictive agent model, which is then used to accurately calibrate the virtual model. When applied to 6 MW turbines, this approach significantly improves real-time performance, accuracy, and reliability, enhancing the overall operational efficiency of wind turbines.

Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a GA-PSO algorithm that combines the Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The GA-PSO algorithm enhances the PSO algorithm by dynamically adjusting the balance between global exploration and local exploitation through a sinusoidal function for the nonlinear adjustment of both learning factors, and an adaptive inertia weight that decreases quadratically with iterations. Additionally, the precision of the solutions is further improved through the crossover and mutation operations of the GA. In the simulated location model, the GA-PSO algorithm demonstrated the smallest error value, outperforming both the GA and PSO algorithm in terms of accuracy. Furthermore, the GA-PSO algorithm exhibited minimal sensitivity to wave speed fluctuations of ±1%, ±3%, and ±5%, maintaining the error within 0.5 m. The validation through the blasting experiment at the Shizhuyuan mine further confirmed the enhanced accuracy of the GA-PSO algorithm, with a location error of 20.08 m, representing an improvement of 59% over the GA and 43% over the PSO algorithm.

The International Maritime Organization (IMO) aims for net zero emissions in shipping by 2050. Ports, key links in the supply chain, are embracing green innovation, focusing on efficient berth and quay crane scheduling to support green port development amid limited resources. Additionally, the energy consumption and carbon emissions from the port shipping industry contribute significantly to environmental challenges and the sustainable development of ports. Therefore, reducing carbon emissions, particularly those generated during vessel berthing, has become a pressing task for the industry. The increasing complexity of berth allocation now requires compliance to vessel service standards while controlling carbon emissions. This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. Numerical experiments using actual data from a container terminal demonstrate the effectiveness and superiority of the PSO-GA algorithm compared to the traditional GA and PSO. The results show a reduction in total operational costs by 24.1% and carbon emissions by 15.3%, highlighting significant potential savings and environmental benefits for port operators. Furthermore, the findings reveal the critical role of tidal factors in improving berth and quay crane scheduling. The results provide decision-making support for the efficient operation and carbon emission control of green ports.

Multilevel inverters have gained importance in modern power systems during the last few years because of their high power quality with lower THD. Various topologies developed include the packed U-cell inverter and its different modified versions that have emerged as a compact and efficient solution to distributed energy systems. Most of the available harmonic mitigation techniques, that is, passive filtering and individual optimization techniques, which include GA and PSO, are susceptible to a variety of shortcomings regarding their inherent complexity and inefficiency; hence, finding an appropriate convergence may be quite hard. This paper proposes a hybrid version of the GA-PSO algorithm that exploits the exploratory strengths of GA and the convergence efficiencies of PSO in determining the optimized switching angles for SHM techniques applied to modified five-level and seven-level PUC inverters. By utilizing the multi-objective optimization method, the approach minimizes THD while keeping voltage and efficiency constraints. Simulated in MATLAB/Simulink, the results were experimentally verified using hardware-in-the-loop testing on OP5700. A large THD reduction in both MPUC7 (11.68%) and MPUC5 (17.61%) was obtained. The proposed hybrid algorithm outperformed the standalone approaches of GA and PSO with respect to robustness and with precise harmonic suppression. Other appealing features are reduced computational complexity and improved waveform quality; hence, the method is highly suitable for both grid-tied and standalone renewable energy applications. This work lays a basis for efficient inverter designs that can adapt well under dynamic load conditions.