Particle Swarm Optimization Search Algorithm Research Articles

A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times

Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times

A Cooperative Silicon Content Dynamic Prediction Method With Variable Time Delay Estimation in the Blast Furnace Ironmaking Process

Online assessment of the molten iron quality of a blast furnace ironmaking process strongly depends on reliable measurement of silicon content (Si). This article proposes a novel cooperative strategy to train the data-driven prediction model and estimate variable time delay (VTD) values. First, a dynamic deep network that stacks denoising autoencoders (D-SDAE) with the ability to describe process nonlinearity and time-varying is designed for Si estimation. Then, time delay between the process variables and Si, which may disrupt the data distribution pattern (i.e., the real input-output relationship), is recovered in the modeling process. It is an overlooked data feature that occurs due to material transportation times, installation location and the analysis period of sampling equipment. The VTD values are considered to be the parameters of the D-SDAE model and obtained in the training process by the proposed double-scale collaborative search particle swarm optimization algorithm. The effectiveness of the proposed VTD-based D-SDAE model is validated in a numerical simulation and an industrial ironmaking plant, and a marked improvement in the prediction performance is achieved when VTD information is considered.

Efficient slope reliability analysis using a surrogate-assisted normal search particle swarm optimization algorithm

Abstract Sampling of training data is the most important step in active learning slope reliability analysis, which controls the analysis accuracy. In this study, a novel surrogate-assisted normal search particle swarm optimization (SANSPSO) was proposed to enhance the accuracy and robustness of existing methodologies. In SANSPSO, the sampling process was considered a minimum problem with an objective function defined as the absolute value of the performance function. Initiated with a normal search paradigm and supplemented by three algorithm strategies, this approach seeks to preserve the continuity of the solution while refining the algorithm’s efficacy and efficiency. To reduce computation cost, surrogate-assistance was used, in which a surrogate model substitutes the objective function in most iterations. This surrogate model evolves during the iteration process and ultimately replaces the actual performance function within Monte Carlo simulation. Finally, this study presents a comparative study with five state-of-the-art methods across four explicit problems and three engineering cases, where test data suggest that the SANSPSO methodology yields a 20% improvement in accuracy and a 30% rise in stability under different dimensional problems relative to the most efficacious of the alternate methods assessed because of the improved and more consistent prediction of limit state function. These findings substantiate the validity and robustness of the SANSPSO approach.

A concurrent design optimization framework for IMSFRP composite structures considering material and structural parameters simultaneously

Injection molded short fiber reinforced polymer (IMSFRP) composite is a widely used lightweight material in the automobile industry. Due to the integrated molding characteristics of IMSFRP composite structures, its lightweight potential can be fully exploited through the simultaneous design of material and structural parameters. However, the efficient material-structure parallel optimization design method for IMSFRP composite structures has not been fully investigated. In this study, a framework of lightweight design containing the concurrent optimization of material and structure parameters is proposed, and it has been successfully applied to an automobile IMSFRP composite liftgate inner. Firstly, considering the skin-core-skin (SCS) structure of the material, a layered model is built up and a parameterized constitutive model is established to efficiently obtain material properties for various material parameters. Then, a parameter extraction and mapping method is proposed to map fiber distribution information to the structural analysis model to improve the accuracy of structural analysis. In addition, based on the Kriging model and a newly developed parallel boundary search particle swarm optimization algorithm, a lightweight design framework of composite liftgate inner is proposed to consider the material and structural design variables simultaneously. The results show that the proposed framework can solve the concurrent optimization design problem effectively and achieve a weight reduction of 10.5 % for the composite liftgate inner under multiple working conditions.

Application of RSAPSO Hybrid Optimizer for Parameter Extraction of Solar PV Cell Models under Temperature Variations

Accurate modeling of photovoltaic (PV) modules/cells is crucial for evaluating the efficiency of solar PV systems. However, the lack of specific parameters of solar cells, which are not included in the manufacturer’s datasheet, often results in flawed cell modeling. Quick and convenient parameter extraction techniques are required to overcome this challenge and create a robust solar PV cell model. These models are useful in optimization, simulation, and enhanced energy harvesting from PV-based renewable energy systems. This paper uses a recent optimizer called Rat Search Algorithm Particle Swarm Optimization (RSAPSO), for extracting the parameters of single diode and double diode models of PV cells. RSAPSO combines the exploration and exploitation advantages of both algorithms. The RSAPSO's parameter optimization results were compared against five other techniques, and the superiority of the suggested algorithm was confirmed through ranking tests, statistical error analyses, and temperature variation sensitivity analyses.

Hybrid gravitational search particle swarm optimization algorithm for GMPPT under partial shading conditions

Solar energy has become one of the popular choices among all renewable energy resources. In order to harvest solar energy, a photovoltaic (PV) system is required. Nowadays, researchers are increasingly paying more attention to PV system since it is affordable and easy to install and maintain. However, unpredictable weather and operating conditions of a PV system may reduce power generation. Therefore, a global maximum power point tracking (GMPPT) controller needs to be installed in the PV system to improve the power generation capability. However, the conventional GMPPT algorithm is less effective because of unsteady oscillations. In this paper, we propose a hybrid method of gravitational search particle swarm optimization (GSPSO) algorithm to track GMPP faster and more efficiently. The proposed algorithm utilizes the exploitation ability of the particle swarm optimization (PSO) algorithm and the exploration ability of the gravitational search algorithm (GSA). Simulation results show that the proposed algorithm has the fastest tracking speed and the highest generated power compared with the other competitive algorithms.

Regional-modal optimization problems and corresponding normal search particle swarm optimization algorithm

Regional-modal optimization problems and corresponding normal search particle swarm optimization algorithm

Joint Optimization of Configuration, Component Sizing, and Energy Management for Input-Split Hybrid Powertrains

Configuration design, component sizing, and energy management are intertwined. However, the full potential of hybrid powertrains has not yet been thoroughly investigated due to the multi-dimensional design space. Therefore, in this paper, a joint optimization method combining particle swarm optimization (PSO) and convex programming (CP) is proposed for the first time. In the outer loop, the PSO algorithm searches for the optimal design for the powertrain configuration. In contrast, the component size and power management are simultaneously optimized by the CP algorithm in the inner loop. A combined driving cycle is designed to integrate both acceleration and energy-saving performance, where an extreme acceleration driving process is intended to act as the acceleration constraint. The results show that, compared to Prius 1st, the acceleration and total cost-saving of the optimal 1-PG (planetary gear) powertrain are improved by 13.09% and 11.08%, respectively. In particular, the acceleration and total cost-saving of the optimal 2-PG powertrain (MG2-RSG) are upgraded by 34.23% and 15.18%, respectively. Finally, the effectiveness and efficiency of the proposed method are verified by comparing it with dynamic programming (DP) and multi-objective evolutionary algorithm based on the decomposition (MOEA/D) method.

A new method of parameter identification for proton exchange membrane fuel cell based on hybrid particle swarm optimization with differential evolution algorithm

With the characteristics of high energy conversion efficiency, high energy den?sity and low operating temperature, the proton exchange membrane fuel cells (PEMFC) have become one of the green energy sources with broad prospects. The establishment of accurate mathematical model of the PEMFC is the basis of simulation and control strategy. At present, some intelligent algorithms have certain drawbacks, and can hardly find the balanced point between precision and computational time. In this study, a novel parameter identification approach com?bining the hybrid particle swarm optimization (PSO) algorithm with differential evolution, i.e. hybrid DEPSO, is proposed to obtain the unknown parameters in the PEMFC mathematical model and solve the problems of premature convergence of PSO and poor global search ability of differential evolution. Six benchmark functions are applied to verify the performance of the algorithm. The results prove that the hybrid DEPSO can evade local optimum preferably while having swifter convergence rate. Two PEMFC stacks are investigated and modeled. In order to evaluate the accuracy of model, the sum of squared errors between the measured voltage and the estimated output voltage are examined. Numerical results show higher accuracy of the hybrid DEPSO-based model comparing with other recently published optimization approaches. Furthermore, the simulation results indicate that the accuracy of the PEMFC model optimized by the hybrid DEPSO algorithm improves 0.19-1.86%, which can provide a new solution the multi-objective opti?mization problem and promote the practical application of the PEMFC.

Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization

There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations. However, accuracy improvement in age estimation studies remains a challenge due to the inability of traditional approaches to effectively capture complex facial features and variations. Therefore, this study investigates the usage of Particle Swarm Optimization in Deep CNN models to improve accuracy. The focus of the study is on exploring different feature extractors for the age estimation task, utilizing pre-trained CNN models such as VGG16, VGG19, ResNet50, and Xception. The proposed approach utilizes PSO to optimize the hyperparameters of a custom output layer for age detection in regression. The PSO algorithm searches for the optimal combination of model hyperparameters that minimize the age estimation error. This study shows that fine-tuning a model can lead to improvements in its performance, with the VGG19 model achieving the best performance after fine-tuning. Additionally, the PSO process was able to find sets of hyperparameters that were on par or even better than the initial hyperparameters. The best result can be seen in VGG19 architecture with loss of 86.181, MAE of 6.693, and MAPE of 38.462. Out of the twelve experiments conducted, it was observed that the utilization of Particle Swarm Optimization (PSO) offered distinct advantages in terms of achieving better results for age estimation. However, it is important to note that the execution time for these experiments was considerably longer when employing PSO.

Predicting the environmental economic dispatch problem for reducing waste nonrenewable materials via an innovative constraint multi-objective Chimp Optimization Algorithm

The usage of conventional fossil fuels has aided fast economic growth while also having negative consequences, such as increased global warming and the destruction of the ecosystem. This paper proposes a novel swarm-based metaheuristic method called Chimp Optimization Algorithm (ChOA) to tackle the environmental, economic dispatch issue and reducing the waste nonrenewable materials. In this regard, two objective functions named fuel cost function and emission cost function are proposed. Unique constrained handling also solves the challenge of multi-objective optimization. Standard IEEE 30 bus with six generators and a 10-unit system are used to demonstrate the usefulness of ChOA. The result of ChOA is compared with Individual Best Memory Penalty Factor Grey Wolf Optimizer (IBMPF-GWO), Improved Whale Trainer (IWT), Chaotic Biogeography-Based Optimizer (CBBO), Non-Linear Migration BBO (NLBBO), Hybrid Gravitational Search Algorithm Particle Swarm Optimization (GSAPSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), Salp Swarm Algorithm (SSA), Dragonfly Algorithm (DA), and Fuzzy Grasshopper Optimization Algorithm (FGOA) to confirm its efficiency. For both single- and multi-objective optimization, ChOA's assessment index and convergence rate are superior to other benchmark algorithms, regardless of whether the goal is to reduce emissions or to reduce fuel costs. The efficacy and robustness of the ChOA in handling environmental economic dispatch problems have been shown by discovering a good compromise value.

Machine-learning-based beam steering in a hybrid plasmonic nano-antenna array

In this paper, different beam-steering techniques are introduced using an 8 × 8 hybrid plasmonic nano-antenna array operating at 1550 nm. Two conventional techniques of the switched-beam and phased array are considered for implementing beam steering. For a switched-beam antenna, the beam steering is achieved by switching between the feed antenna elements, whereas the feed antenna array is attached to the back surface of a fishnet achromatic-metalens to collimate the rays. In the phased array antenna, the beam is steered by estimating the appropriate feeding phases of the 64 elements using a deep neural network (DNN) either with or without a lens and comparing the results with those obtained using a traditional method. Finally, a hybrid technique based on activating only a subset of antenna elements in the existence of the lens is proposed to steer the pattern in a certain direction. By predicting the proper feeding phases of the antenna array elements, the neural network with the backpropagation technique and weighted hybrid gravitational search algorithm-particle swarm optimization approach is used to beam-steer the pattern. Furthermore, the DNN is applied to assign the required active subset elements for directing the main beam toward the desired direction. Several sample examples are provided to beam-steer the pattern in numerous directions to assess the correctness of the strategies.

A novel hybrid gravitational search particle swarm optimization algorithm

Particle Swarm Optimization (PSO) algorithm is a member of the swarm computational family and widely used for solving nonlinear optimization problems. But, it tends to suffer from premature stagnation, trapped in the local minimum and loses exploration capability as the iteration progresses. On the contrary, Gravitational Search Algorithm (GSA) is proficient for searching global optimum, however, its drawback is its slow searching speed in the final phase. To overcome these problems in this paper a novel Hybrid Gravitational Search Particle Swarm Optimization Algorithm (HGSPSO) is presented. The key concept behind the proposed method is to merge the local search ability of GSA with the capability for social thinking (gbest) of PSO. To examine the effectiveness of these methods in solving the abovementioned issues of slow convergence rate and trapping in local minima five standard and some modern CEC benchmark functions are used to ensure the efficacy of the presented method. Additionally, a DNA sequence problem is also solved to confirm the proficiency of the proposed method. Different parameters such as Hairpin, Continuity, H-measure, and Similarity are employed as objective functions. A hierarchal approach was used to solve this multi-objective problem where a single objective function is first obtained through a weighted sum method and the results were then empirically validated. The proposed algorithm has demonstrated an extraordinary performance per solution stability and convergence.

Taguchi-EM-AI Design Optimization Environment for SynRM Drives in Traction Applications

Multi-objective design optimization environments are used for electric vehicles and other traction applications to arrive at efficient motor drives. Typically, the environment includes characterization modules that involve the use of Electromagnetic Finite Element and State-Space models that require large number of iterations and computational time. This work proposes the utilization of a Taguchi orthogonal arrays method in conjunction with a Particle Swarm Optimization search algorithm to reduce computational time needed in the design optimization of electric motors for traction applications. The effectiveness of the Taguchi method in conjunction with the optimization environment is demonstrated in a case study involving a prototype of a Synchronous Reluctance Motor drive system.

Thermo-economic analysis and optimization of the steam absorption chiller network plant

Absorption chillers are one of the most used equipment in industrial, commercial, and domestic applications. For the places where high cooling is required, they are utilized in a network to perform the cooling demand. The main objective of the current study was to find the optimum operating conditions of a network of steam absorption chillers according to energy and economic viewpoints. Firstly, energy and economic analysis and modelling of the absorption chiller network were carried out to have a deep understanding of the network and investigate the effects of operating conditions. Finally, the particle swarm optimization search algorithm was employed to find an optimum levelized total costs of the plant. The absorption chiller network plant of the Marun Petrochemical Complex in Iran was selected as a case study. To verify the simulation results, the outputs of energy modelling were compared with the measured values. The comparison with experimental results indicated that the developed model could predict the working condition of the absorption chiller network with high accuracy. The economic analysis results revealed that the levelized total costs of the plant is 1730 $/kW and the pay-back period is three years. The optimization findings indicated that working at optimal conditions reduces the levelized total costs of the plant by 8.5%, compared to the design condition.

OPTIMIZATION-DRIVEN DEEP RECURRENT NEURAL NETWORK FOR INTRUSION DETECTION AND HEALTH RISK ASSESSMENT IN WIRELESS BODY SENSOR NETWORK

Wireless body sensor network (WBSN) has gained great attention in the environmental and military applications, but security is the major issue, nowadays. In addition, the data exchanged through the wireless sensor network (WSN) is vulnerable to several malicious attacks because of the physical defense equipment needs. Hence, various intrusion detection methods are required for defending against such attacks. Accordingly, an effective method, named deep recurrent neural network (Deep RNN), is proposed in this research for detecting the intrusion in WBSN. At first, the WBSN nodes are utilized to sense the data from the health records of patient for acquiring certain parameters to make risk assessment. Then, WBSN nodes transmit the data to the target nodes using the obtained parameters. After the determination of parameters, the WBSN nodes are responsible to collect the information of the patient and transfer the obtained information to cluster heads (CHs) based on the hybrid harmony search algorithm–particle swarm optimization (HSA–PSO). HSA–PSO is utilized for identifying the optimal CH node iteratively. From the selected CHs, secure communication is done to exchange the data packets. After that, the KDD features are extracted and intrusion detection is done using the proposed Deep RNN. After the genuine users are detected, the classification is done using fractional cat-based salp swarm algorithm (FCSSA) for the risk assessment. The performance of the intrusion detection and health risk assessment in WBSN based on the proposed model is evaluated based on accuracy, sensitivity, and the specificity. The developed model achieves the maximal accuracy of 95.79%, maximal sensitivity of 95.97%, and the maximal specificity of 95.61% using Cleveland dataset.

Real Time Optimal Tuning of Quadcopter Attitude Controller Using Particle Swarm Optimization

A real-time novel algorithm for proportional, integral and derivative (PID) controller tuning for quadcopters is introduced. The particle swarm optimization (PSO) method is utilized to search the quadcopter solution space to find the best PID controller parameters. A fuzzy logic (FL) controller is used to provide proper velocity reference signals to serve as tracking set points to be achieved by the PID controller. This nested loop design is proposed for stabilizing the quadcopter, where the fuzzy logic controller (FL) is used in the stable loop (i.e. outer loop) to control the desired angle, while the PID controller is used for the rate loop (i.e. inner loop). Finally, the optimum generated PID parameters were achieved in real time using the PSO search algorithm. The generated parameters were tested successfully using an experimental quadcopter setup at the University of Jordan.

The magnetic binary lithium clusters W2Lin (n = 15‐19): A theoretical prediction of “di‐superatomic molecules”

AbstractBased on the super valence bond (SVB) model, a motif of face‐sharing bi‐tetrahexahedral superatomic molecules M1M2@Li20 (M1/M2 = Ti and W) was recently constructed by fusion of two tetrahexahedral superatoms Ti@Li14 and W@Li14. Here, as an extension of the formation of molecules from superatoms, binary lithium clusters W2Lin (n = 14‐19) are studied in conjunction with particle swarm optimization algorithm search and density functional theory calculations. The lowest‐lying isomers of W2Lin (n = 15‐19) with the magnetic moments of 0 to 3 μB are identified as superatomic molecules by analysis of their molecular orbitals and chemical bonding patterns, whereas the global minimum isomer of W2Li14 does not possess the geometric structure of monomer. Via a lithium atom sequentially removed, a series of superatomic bond orders of 3, 3.5, 4, and 4.5 are exposed, reminiscent of classical chemical bonds. Meanwhile, many isosupermolecules are found in the low‐energy structures, predicting the diversity assembly of superatom materials. Our results highlight the tremendous opportunities for molecules assembled by superatoms, which may be in favor of the designs of nanoparticles or functional materials in future according to the SVB model.

Application of particle swarm optimization to water management: an introduction and overview.

Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO's applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall-runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.