Conventional Particle Swarm Optimization Research Articles

Fault Prediction Modeling for High-Impact Recorders Based on IPSO-SVM

The challenge in reusing high-impact recorders lies in developing an efficient and accurate failure prediction model under small-sample conditions. To address this issue, this study proposes an IPSO-SVM model. First, the particle swarms in the IPSO algorithm were grouped based on their exploration and exploitation functions, and dynamic inertia weight mechanisms were designed accordingly. The grouping ratio was dynamically adjusted during iterations to enhance optimization performance. Tests using benchmark functions verified that this approach improves convergence accuracy and stability compared to conventional PSO algorithms. Subsequently, the 5-fold cross-validation accuracy of the SVM model was used as the fitness value, and the IPSO algorithm was employed to optimize the penalty and kernel parameters of the SVM model. Trained on high-impact experimental data, the IPSO-SVM model achieved a prediction accuracy of 90.5%, outperforming the PSO-SVM model’s 85%. These results demonstrate the potential of the IPSO-SVM model in addressing failure prediction challenges under small-sample conditions.

A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system

This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA’s reference voltage output. The LADRC, known for managing uncertainties and disturbances, improves the anti-interference capacity of the maximum power point tracking (MPPT) technique and speeds up the system’s response rate. Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. Finally, the experimental investigation of the proposed HHOA-LADRC was accomplished with the NI PXIE-1071 Hardware-In-Loop (HIL) prototype. The output findings show that the effectiveness of the provided HHOA-LADRC may approach a value higher than 99%, showed a quicker rate of converging and less oscillations in power through the detection mechanism.

Enhanced dynamic inertia particle swarm optimization with velocity clamping for accurate parameter extraction in one-diode and two-diode solar PV models

Purpose Accurate solar photovoltaic models (SPVM) are critical for optimizing solar photovoltaic (PV) capacity to convert sunlight into electricity. The simulation and design of PV systems rely on estimating unknown constraints from solar photovoltaic (SPV) cells. Each parameter plays a crucial task in the output properties of an SPV under actual environmental conditions. Optimizing the unknown constraints of the SPVM is not an easy task due to the nonlinear characteristics of the PV cell. This study aims to develop a novel metaheuristic algorithm, enhanced dynamic inertia particle swarm optimization (EDIPSO) algorithm with velocity clamping, to establish all the seven and five constraints of the two-diode model (TDM) and one-diode model (ODM). Design/methodology/approach In complex parameter spaces, the conventional particle swarm optimization (PSO) approach typically leads to poor convergence because it fails to balance exploration and exploitation. The proposed approach is an EDIPSO with velocity clamping to minimize the possibility of overshooting possible solutions and improve stability. Velocity clamping is also used to prevent particle velocities from rising over specified limitations. Beginning the process with a large inertia weight to promote exploration and progressively decreasing it to improve exploitation, leading to a thorough analysis of the search space. The algorithm is implemented to investigate the accuracy of estimated constraint values of RTC-France (RTC-F) solar cell, Photo watt-PWP 201 SPV module (PWP 201 SPV), KC 200GT SPV module (KC 200 GT SPV) for ODM and TDM. Findings The proposed approach is used to extract the seven and five constraints of the TDM and ODM under standard test conditions for three different SPV modules. Thorough simulation and statistical analysis indicate that the EDIPSO with velocity clamping may outperform other cutting-edge optimization algorithms exclusively regarding accuracy, computational time and reliability. Originality/value An enhanced dynamic inertia PSO is suggested for determining the parameters of the TDM and ODM in SPV modules. This method specifically accounts for the recombination saturation current within the p–n junction’s depletion region, without overlooking or assuming away any parameters, thereby achieving greater accuracy. When comparing the estimated constraints of TDM and ODM for various SPVs, EDIPSO almost precisely aligns the data from the proposed model with the practical data. Thus, the proposed method for calculating the SPV model parameter may exhibit to be a feasible and efficient solution.

A Genetic Particle Swarm Optimization with Policy Gradient for Hydraulic Fracturing Optimization

Summary Particle swarm optimization (PSO) has been widely developed and applied as one of the important algorithms for optimization problems because of its few control parameters and low complexity. However, conventional PSO has the problem of premature convergence. Most of the control parameters of optimized PSO are adjusted manually, and the technology of adaptive adjustment based on reinforcement learning (RL) is still immature. Therefore, this study proposes a new genetic PSO (GAPSO) with policy gradient (PGGAPSO). The algorithm constructs a strategy neural network as an agent to dynamically output the action probability distribution and passes the action with the highest selection probability to the GAPSO section through the designed action selection function, thereby updating the parameters. The GAPSO section solves the optimization problem based on the new parameters obtained and uses the individual and global optimums obtained from the iterations as inputs to the policy neural network. In addition, an elite strategy is introduced to enhance the exploration capability of the PGGAPSO. Then, the performance of PGGAPSO was comprehensively evaluated using convergence analysis, statistical tests, Friedman rank, and runtime analysis. Finally, PGGAPSO is applied to the construction parameter optimization of hydraulic fracturing. The results show that due to the influence of the number of samples, the optimization performance of PGGAPSO is general in the early stage of iteration but shows excellent with continuous iteration. For unimodal and most of multimodal functions, PGGAPSO shows significant difference and excellence. Additional processes, such as elite strategy and policy gradient (PG), resulted in a 7.3% increase in runtime for PGGAPSO compared with GAPSO, but this additional computational loss is acceptable relative to the improved accuracy. The algorithm does not require an objective function and can be combined with the complex agent model of hydraulic fracturing to optimize the fracturing parameters. In the case of parameter optimization for hydraulic fracturing in the Jimsar sag, the optimized production was increased by more than 10% on average.

Fitness and historical success information-assisted binary particle swarm optimization for feature selection

Feature selection is a critical preprocessing step in machine learning aimed at identifying the most relevant features or variables from a dataset. Although conventional particle swarm optimization (PSO) has shown efficiency for feature selection tasks, developing an effective PSO algorithm for this task is still challenging. This study proposes a fitness and historical success information-assisted binary particle swarm optimization, denoted by FPSO. The FPSO is developed by integrating different search strategies, including a weighted center-based approach, historical information-based acceleration coefficients, and selection operation. These strategies are embedded into the FPSO to enhance the levels of exploration and exploitation based on the fitness value of particles and their historical search status. In the FPSO, the transfer function is also added to transform the continuous search space into binary search space. Experimental validation and comparison with seven other metaheuristic algorithms on 24 datasets verify the effectiveness of the FPSO in eliminating irrelevant and redundant features.

Reinforcement learning-based particle swarm optimization for wind farm layout problems

Optimizing wind farm layouts is critical to maximizing wind power generation. The wake effect significantly impacts turbines located downwind, making farm layout a key determinant of power generation efficiency. Traditional algorithms often overlook the value of leveraging historical information, which can lead to entrapment in local optima. Our survey reveals that previous studies on wind farm layout optimization (WFLO) have not adequately integrated the historical data of particle swarm optimization (PSO) with reinforcement learning’s empirical pool, resulting in the loss of valuable information. Here, we present a novel approach that enhances algorithm development and exploration by utilizing historical data and integrating proximal policy optimization from reinforcement learning with an experience pool. This method markedly outperforms the conventional genetic PSO in terms of performance. Extensive numerical experiments across wind farms of various sizes and four distinct wind scenarios demonstrate the superior efficacy of our reinforcement learning-based particle swarm optimization (RPSO) algorithm compared to 12 state-of-the-art methods. Under four wind scenarios, the average power conversion efficiencies of RPSO for the three turbine scales reach 98.68%, 98.14%, and 97.33%, respectively, underscoring the high competitiveness of the proposed RPSO for WFLO in diverse wind conditions.

Path Planning for Unmanned Systems Based on Integrated Sampling Strategies and Improved PSO

Abstract B-splines and Particle Swarm Optimization algorithms are integrated for unmanned system path planning in mountainous terrains. In the early stages of the optimization search, the traditional Particle Swarm Optimization (PSO) algorithm achieves rapid convergence. However, as the process continues, it often struggles with local optima in later stages. To address this limitation, this research proposes an improved PSO algorithm that combines the Immune Algorithm (IMA) and Latin Hypercube Sampling Method. This enhancement bolsters the optimization capabilities of particles at different phases of the search by implementing an evaluation mechanism and dynamic weight adjustments. Experimental results demonstrate that, when confronting optimization challenges within complex mountainous terrains, the improved PSO algorithm (SIPSO) which is combined with IMA and Sampling Method significantly outperforms conventional PSO and Genetic Algorithm (GA) in both iteration counts and computational efficiency, showcasing a notable advancement in performance.

Advanced parameter extraction optimization technique for the four-diode model approach

Accurate performance estimation of photovoltaic devices is important in optimizing the efficiency and cost of the photovoltaic systems. The one-diode and two-diode models are used because they concisely represent the current-voltage (I-V) variations. However, these models mainly focus on the fundamental mechanism of diffusion and Shockley-Read-Hall recombination. Four-diode model (FDM) is developed from the standard three-diode model used to enhance the precision of PV system performance estimations. However, the better-detailed FDM offers modeling of other recombination and leakage currents that occur in rather complex solar cells or advanced cells like heterojunction, multijunction, or perovskite ones. This model helps to get a more accurate picture of the PV cell operation as several diodes are added to model recombination processes and defects. This work makes use of sophisticated forms of parameter extraction aimed at promoting the optimization of algorithms such as the one known as Advanced Dynamic Inertia-Particle Swarm Optimization with Velocity Clamping or ADIPSO-VC. For comparison with FDM, a three-diode model (THDM) is utilized, and the outcome of the former is then analyzed against the latter. In addition, as a confirmation of the reliability and repeatability of the results obtained by applying the developed algorithm for parameter extraction, FDM is compared with classical methods. To demonstrate the efficacy of the proposed method it is tested against the other algorithms Simulated annealing, and conventional PSO. Based on the comparison, it is evident that ADIPSO-VC surpasses the other methods by demonstrating lower error rates and shorter computational time.

Preserving privacy in association rule mining using multi-threshold particle swarm optimization

Healthcare data has become a powerful resource for generating insights that drive medical research. Association Rule Mining (ARM) techniques are widely used to identify relationships among diseases, treatments, and symptoms. However, sensitive information is often exposed, creating significant privacy challenges, particularly when data is integrated from multiple sources. Although Privacy-Preserving Association Rule Mining (PPARM) methods have been developed to address these issues, most rely on a single, predefined Minimum Support Threshold (MST) that is inflexible in adapting to diverse rule patterns. In this study, a Multi-Threshold Particle Swarm Optimization for Association Rule Mining (MPSO4ARM) model is introduced, integrating the Apriori and Particle Swarm Optimization (PSO) algorithms to perform data mining while protecting sensitive rules. A novel approach is employed by the proposed model to dynamically adjust the MST, allowing for more adaptive and effective privacy preservation. The MPSO4ARM model adjusts the MST on-the-fly based on rule length, improving its ability to safeguard sensitive data across various datasets. The proposed model was evaluated on the Chess, Mushroom, Retail, and Heart Disease datasets. The experimental results showed that the MPSO4ARM model outperforms traditional Apriori and conventional PSO algorithms, achieving higher fitness values and reducing side effects such as Hiding Failure (HF) and Missing Cost (MC), particularly in the Heart Disease and Mushroom datasets. Although the dynamic MST function introduces a moderate increase in computational runtime compared to Apriori and conventional PSO, this trade-off between execution time and enhanced privacy protection is considered acceptable, given the model's substantial improvements in data utility and rule sanitization.

Design Optimization of a Plate‐Fin Heat Exchanger With Metaheuristic Hybrid Algorithm

ABSTRACTThis paper introduces a novel approach to enhance the heat transfer efficiency of a plate‐fin heat exchanger. The metaheuristic hybrid approach combines particle swarm optimization (PSO) with the genetic algorithm (GA). Seven critical design parameters are used as variables. The research demonstrates the effectiveness of this hybrid method through a case study based on existing literature. The numerical results show the superior performance of the hybrid genetic algorithm particle swarm optimization over conventional GA and PSO techniques. The hybrid method achieves an optimal configuration with increased accuracy in a shorter computational time, offering significant time and cost savings in the design process.

Fouling Prediction of a Heat Exchanger Based on Wavelet Neural Network Optimized by Improved Particle Swarm Optimization Algorithm

The relevant experimental data of the fouling formation process of a heat exchanger were obtained through the fouling monitoring experimental platform. Whereafter, with regard to the conventional particle swarm optimization (PSO) algorithm, this study commenced from the iteration formula and innovatively presented an optimization approach for improving the inertia weight, thereby obtaining the improved particle swarm optimization (IPSO) algorithm. The wavelet neural network (WNN) was optimized through the application of the IPSO–WNN algorithm, resulting in the development of the IPSO–WNN model. Utilizing this model, a predictive model for fouling thermal resistance was constructed, incorporating input variables such as conductivity, pH, dissolved oxygen, average wall temperature, and bulk temperature, while the output variable represented fouling thermal resistance. Comparative analyses demonstrated that the IPSO–WNN model exhibited superior prediction accuracy and robust generalization capabilities to that of the conventional WNN and PSO–WNN models, as evidenced by significantly lower values across all indicators, including MAPE, MAE, and RMSE. The IPSO algorithm effectively optimized the initial parameters of the WNN, addressing the challenge of local minimum and enhancing the model’s overall capacity to identify optimal solutions. This model effectively captures the dynamic trends of fouling thermal resistance during its growth stage and approaches the asymptotic value in the stable stage. Precise prediction models for heat exchanger fouling contribute valuable insights for its prediction in practical industrial applications.

Prediction of permeability characteristics of mine slurries and sediments using finite-strain framework by optimisation algorithms

Determining the permeability characteristics of mine tailing slurries through laboratory finite-strain consolidation tests is costly due to the extensive testing required. An alternative approach involves back-predicting hydraulic conductivities from settling column test data. This study employed settlement and excess pore pressure data as inputs in optimising the permeability parameters using the finite-strain consolidation (FSC) framework. The proposed approach used the finite difference method to solve the governing equation for FSC in the forward analysis. Two novel variants of the Artificial Bee Colony (ABC) algorithm, namely the Modified Artificial Bee Colony (MABC) and the Hybrid Artificial Bee Colony (HABC) algorithms, were used to back-predict the hydraulic conductivity function parameters from the FSC test data. These models predicted the permeability characteristics using the temporal variation of the settlements for six slurries and excess pore pressure dissipation data for four slurries. The performance of the proposed algorithms along with Hybrid Particle Swarm Optimisation (HPSO) was compared with the conventional optimisation algorithms, viz., PSO, ABC, and Quantum PSO (QPSO). The HABC and HPSO exhibited remarkable stability, consistently converging to identical solution sets across multiple iterations, thereby outperforming other algorithms in this study. Despite its higher time complexity by HABC relative to the other evaluated methods, this complexity is warranted for enhanced robustness. The current study is helpful in brining practical and reliable methodologies for estimating the hydraulic conductivity function from field-scale conductivity (FSC) data, providing critical insights for the safe and efficient management of slurry wastes.

An exponential variation based PSO for analog circuit sizing in constrained environment

This work presents an Exponential Variation based Particle Swarm Optimization (EV-PSO) algorithm to improve the convergence rate and find an optimal solution to analog circuit optimization problems in a constrained-driven environment. Existing evolutionary algorithms have a lower convergence rate leading to higher design time. This work introduces two novel parameters, ζ1 and ζ2, into the velocity update equation. These parameters dynamically vary with the number of iterations. The algorithm was implemented on the Python platform. The results have shown that, in comparison to the considered existing methods, the exponential variation of the parameters ζ1 and ζ2 in the proposed algorithms have a larger rate of convergence. The proposed EV-PSO has a convergence rate of 27 iterations, which is 57.8%, 65.38%, and 59.1% better than the conventional PSO, differential evolution (DE) and genetic algorithm (GA) respectively. The typical design obtained from the optimal solution is verified through the simulation using 45-nm CMOS technology. The optimal solution presented in this work meets the desired input specifications within the specified constrained environment.

Automatic optimization design of laser triangulation ranging sensors using an improved genetic algorithm

Optical system parameter design is of great importance to ensure the accuracy of asymmetry systems such as laser triangulation ranging systems. However, the system parameter determination often depends on the experience and manual attempts of designers, which is not only time-consuming but also inevitable to introduce human errors. Therefore, in this paper an automatic optimization design method based on nonlinear programming genetic algorithm with elitism strategy (E-NPGA) is proposed, to accurately and fast determine the optimal system parameters of laser triangulation ranging systems assisting in improving the measurement accuracy. Firstly, an optimization model of system parameters is developed under the Scheimpflug rule establishing the constraints for various measurement resolutions and ranges. An image size constraint is constructed for the first time to improve and evaluate the parameter optimization. Secondly, the E-NPGA is proposed with nonlinear optimization and elitism strategy, which can determine the optimal system parameters in 15 iterations avoiding local extremum. In design examples, using the E-NPGA determined system parameters ZEMAX simulation and experimental results of the parameters depended image spot size show a slight relative difference below 0.6%. Moreover, the experiment results demonstrate the sensor system designed by using the E-NPGA enables a distance measurement with submicron absolute error and 10−4 relative uncertainty. The automatic optimization method proposed in this paper is compared with the conventional GA method and PSO method, and it is validated that the convergence accuracy of the proposed method is much higher than the conventional ones.

Multi-Objective Energy Management in Microgrids: Improved Honey Badger Algorithm with Fuzzy Decision-Making and Battery Aging Considerations

A multi-objective energy management and scheduling strategy for a microgrid comprising wind turbines, solar cells, fuel cells, microturbines, batteries, and loads is proposed in this work. The plan uses a fuzzy decision-making technique to reduce pollution emissions, battery storage aging costs, and operating expenses. To be more precise, we applied an improved honey badger algorithm (IHBA) to find the best choice variables, such as the size of energy resources and storage, by combining fuzzy decision-making with the Pareto solution set and a chaotic sequence. We used the IHBA to perform single- and multi-objective optimization simulations for the microgrid’s energy management, and we compared the results with those of the conventional HBA and particle swarm optimization (PSO). The results showed that the multi-objective method improved both goals by resulting in a compromise between them. On the other hand, the single-objective strategy makes one goal stronger and the other weaker. Apart from that, the IHBA performed better than the conventional HBA and PSO, which also lowers the cost. The suggested approach beat the alternative tactics in terms of savings and effectively reached the ideal solution based on the Pareto set by utilizing fuzzy decision-making and the IHBA. Furthermore, compared with the scenario without this cost, the results indicated that integrating battery aging costs resulted in an increase of 7.44% in operational expenses and 3.57% in pollution emissions costs.

A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells

Fuel cells are one of the most promising renewable energy sources, offering advantages like reliability, eco-friendliness, and low pollutant emissions, which have spurred rapid advancements in power generation technologies. However, fuel cells face significant challenges, including high initial costs, limited fuel availability, and the difficulty of maintaining operation at the maximum power point, which hinders their use in stand-alone applications. In this paper, a Modified Particle Swarm Optimization (MPSO) method is proposed for maximum power point tracking (MPPT) to optimize the power output of Proton Exchange Membrane Fuel Cells (PEMFCs). The proposed method dynamically adjusts to key operational parameters such as cell temperature, hydrogen partial pressure, and membrane water content, areas that have not been comprehensively addressed in previous research. In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. Under rapid temperature fluctuations in the fuel cell, the proposed MPSO MPPT method achieved a maximum power of 1223.5 W with an average of 5.66 iterations. In comparison, the meta-heuristic particle swarm optimization (PSO) method and the conventional perturb and observe (P&O) method achieved maximum power outputs of 1218.5 W and 1213.65 W, respectively, with PSO requiring 12.33 iterations. Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. The MPSO method is implemented in the Simulink/MATLAB environment and is compared with the Perturb & Observe (P&O) and Conventional PSO (CPSO) methods. The results demonstrate that the proposed MPSO approach outperforms these traditional techniques in terms of tracking speed, efficiency, and stability under varying conditions. This successful implementation lays a strong foundation for future integration into real-world PEMFC systems.

Participation of wind-storage combined system in the secondary frequency regulation control strategy of power grid

Abstract The secondary frequency modulation of the unit, that is, the automatic generation control (AGC), is the process of increasing or decreasing the power of the power generation unit by the power plant unit according to the dispatching command issued by the power grid dispatching center, and then using the frequency modulator to restore the frequency. Based on the principle of AGC, this paper establishes a frequency dynamic response model of the secondary frequency modulation of the traditional unit and the wind storage participation system including load frequency control (LFC) and traditional proportional-integral-derivative (PID) controllers, determines the frequency modulation control strategy of wind storage, and uses the distribution mode based on Area Control Error (ACE) signal to assist in the secondary frequency modulation. In order to solve the parameter optimization problem of the classical PID controller when the wind-storage combined system participates in the secondary frequency modulation under the large load disturbance, a parameter optimization model of the controller with PID was established, and the ITAE evaluation index was finally selected as the fitness function by comparing different fitness evaluation indexes. To overcome the problem that the conventional Particle Swarm Optimization (PSO) is prone to local extremum, the inertia weights and learning factors are adjusted nonlinearly to balance and optimize the global and local optimization capabilities and improve the search efficiency, and the parameters of the PID controller are optimized by the improved PSO algorithm. The simulation results show that the proposed algorithm improves the secondary frequency modulation effect under the background of a high proportion of renewable energy connected to the power grid.

Coronal deformity in ankylosing spondylitis with concomitant thoracolumbar kyphosis: patterns, manifestations and surgical strategies.

To evaluate different patterns of coronal deformity secondary to ankylosing spondylitis (AS), to propose relevant treatment strategies, and to assess efficacy of asymmetrical pedicle subtraction osteotomy (APSO). Coronal deformity was defined as coronal Cobb angle over 20º or coronal balance distance (CBD) more than 3cm. 65 consecutive AS patients with concomitant coronal and sagittal deformity who underwent PSO were included. The average follow-up time was 40.4 months. Radiographic evaluation included coronal Cobb angle and CBD. Furthermore, sagittal parameters were used to assess magnitude and maintenance of kyphosis correction. Based on curve characteristics, coronal deformity caused by AS included four different radiologic patterns: Pattern I: lumbar scoliosis; Pattern II: C-shaped thoracolumbar curve; Pattern III: trunk shift without major curve; Pattern IV: proximal thoracic scoliosis. APSO was performed for patients in Pattern I to III while conventional PSO was applied for patients in Pattern IV. Significant improvement in all the sagittal parameters were noted in 65 patients without obvious correction loss at the last follow-up. Besides, significant and sustained correction of coronal mal-alignment was identified in 59 APSO-treated patients. Rod fracture occurred in four cases and revision surgery was performed for one case. According to radiologic manifestations, coronal deformity caused by AS could be categorized into four patterns. APSO proved to be a feasible and effective procedure for correction of Pattern I to III patients. Coronal deformity pattern, apex location, sagittal profile of lumbar spine and preoperative hip function should be considered for osteotomy level selection in APSO.

An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock’s direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.

Cost-Effective Planning of Hybrid Energy Systems Using Improved Horse Herd Optimizer and Cloud Theory under Uncertainty

In this paper, an intelligent stochastic model is recommended for the optimization of a hybrid system that encompasses wind energy sources, battery storage, combined heat and power generation, and thermal energy storage (Wind/Battery/CHP/TES), with the inclusion of electric and thermal storages through the cloud theory model. The framework aims to minimize the costs of planning, such as construction, maintenance, operation, and environmental pollution costs, to determine the best configuration of the resources and storage units to ensure efficient electricity and heat supply simultaneously. A novel meta-heuristic optimization algorithm named improved horse herd optimizer (IHHO) is applied to find the decision variables. Rosenbrock’s direct rotational technique is applied to the conventional horse herd optimizer (HHO) to improve the algorithm’s performance against premature convergence in the optimization due to the complexity of the problem, and its capability is evaluated with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) methods. Also, the cloud theory-based stochastic model is recommended for solving problems with uncertainties of system generation and demand. The obtained results are evaluated in three simulation scenarios including (1) Wind/Battery, (2) Wind/Battery/CHP, and (3) Wind/Battery/CHP/TES systems to implement the proposed methodology and evaluate its effectiveness. The results show that scenario 3 is the best configuration to meet electrical and thermal loads, with the lowest planning cost (12.98% less than scenario 1). Also, the superiority of the IHHO is proven with more accurate answers and higher convergence rates in contrast to the conventional HHO, PSO, and MRFO. Moreover, the results show that when considering the cloud theory-based stochastic model, the costs of annual planning are increased for scenarios 1 to 3 by 4.00%, 4.20%, and 3.96%, respectively, compared to the deterministic model.