Accelerated Particle Swarm Optimization Research Articles

A Novel Interpolated DFT Synchrophasor Estimation Algorithm With an Optimized Combined Cosine Self-Convolution Window

With the current power system being electronicized nowadays, synchrophasor estimation (SE) plays an increasingly critical role in the monitoring, analysis, and protection of the state of the power grid. One proposal has been brought to enhance the commonly used discrete Fourier transform (DFT) algorithm that has a large error due to the inherent spectral leakage and fence effect in asynchronous sampling by interpolations. This article proposes an SE algorithm based on an optimized combined cosine self-convolution (OCCSC) window. Using the sidelobe characteristics of the window functions as the evaluation index, an optimized combined cosine window is obtained by the accelerated particle swarm optimization (APSO) algorithm. The window function’s self-convolution is used to further expand its excellent characteristics. Based on these characteristics, combined with interpolated DFT, simulations and experimental verifications compared with other algorithms show the effectiveness of the proposed algorithm.

Optimizing connection weights of functional link neural network using APSO algorithm for medical data classification

Classification is a common problem in various fields of life, and the key challenging task in data mining. The primary objective of the classification process is to classify the given dataset in a defined class label for all data. Many research papers widely used classification in the medical sector. Various researches have been carried out to classify medical data using different techniques, such as High Order Neural Network (HONN) combined with Back-Propagation Neural Network (BPNN) as a learning algorithm. Due to the increased data complexity, the back-propagation algorithm faced problems of slow convergence. The Back-Propagation (BP) using the gradient descent technique has the possibility of getting stuck in local minima. So, the BP algorithm may cause difficulties in finding the global minima of the error function. This paper proposed a high-order Functional Link Neural Network (FLNN). The proposed FLNN model is integrated with a metaheuristic-based searching algorithm called Accelerated Particle Swarm Optimization (APSO). The performance of the proposed Accelerated Particle Swarm Optimization Functional Link Neural Network (APSOFLNN) model is validated with various medical datasets and compared to traditional techniques such as Accelerated Particle Swarm Optimization Functional Link Back-Propagation (APSOFLBP), and Artificial Bee Colony Functional Link Neural Network (ABCFLNN). The simulation results showed that the proposed APSOFLNN algorithm for the used benchmarked datasets shows good results in terms of the mean square error (MSE) and accuracy in comparison to the traditional techniques.

Modified Particle Swarm Optimization With Chaotic Attraction Strategy for Modular Design of Hybrid Powertrains

This article proposes a new modular design method for hybrid powertrains using a modified accelerated particle swarm optimization (MAPSO) algorithm. The method determines the optimal combination of component specifications and control parameters, where the component specifications include integer variables (e.g., the number of battery modules). A unified chaotic attraction strategy for MAPSO is developed based on a logistic map to improve the probability of achieving the global optimal result. The Pareto analysis is carried out to identify the weighting value for the tradeoff in modular design. The comprehensive reputation score (CRS), considering both Monte Carlo results and the probability of achieving global optima, is employed to evaluate the advantages of the MAPSO compared with conventional PSO and four other PSO variants. The MAPSO is verified as the best because it has the highest CRS. Both two-level and simultaneous methods for modular design are developed with the MAPSO, where the former first operates component sizing at the level 1 and then conducts control optimization at the level 2, and the later optimizes the size and control simultaneously. Compared with the two-level method, the simultaneous method achieves a 7% higher cost function value and saves 50% time.

Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units

The energy management system (EMS) of an electrical distribution system (EDS), with the integration of distributed generation (DG) and distribution static compensator (DSTATCOM), provides numerous benefits and significantly differs from the existing EDSs. This paper presents an optimal integration of DG based on photovoltaic (PV) solar panels and DSTATCOM in EDS. A single objective function, based on maximizing the active power loss level (APLL) in EDS, is deployed to find the optimal size and location of photovoltaic DG and DSTATCOM simultaneously in different study cases using various particle swarm optimization (PSO) algorithms. These PSO algorithms are the basic PSO, adaptive acceleration coefficients PSO (AAC-PSO), autonomous particles groups for PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). These algorithms are applied to the standard IEEE 33- and 69-bus EDSs, which are used as test systems to verify the effectiveness of the proposed algorithms. Simulation results prove that the TVA-PSO algorithm exhibits higher capability and efficiency in finding optimum solutions. Comparing the simulation results attained for different study cases leads to the conclusion that DG and DSTATCOM were optimally-allocated simultaneously, which resulted in a significant reduction of power losses and an enhancement of the voltage profile.

Optimized multi-output machine learning system for engineering informatics in assessing natural hazards

This work develops a novel metaheuristic optimization-based least squares support vector regression (LSSVR) model with a multi-output (MO) algorithm for assessing natural hazards. The MO algorithm is more efficient than the single-output algorithm because the relations among outputs can be estimated simultaneously by the proposed prediction model. Furthermore, the hyperparameters in MOLSSVR are optimized using an accelerated particle swarm optimization (APSO) algorithm combined with a self-tuning method to generate the best predictions and the fastest convergence. The APSO algorithm is validated by solving benchmark functions with unimodal and multimodal characteristics. The performance of APSO-MOLSSVR is compared with those of hybrid and single models yielded from standard multi-input single-output algorithms. A graphical user interface was designed as a stand-alone application to provide a user-friendly system for executing advanced data mining techniques. In real-world engineering cases, APSO-MOLSSVR achieved an error rate that was up to 63.55% better than those achieved using prediction models that are proposed in the single-output scheme. The system much more quickly and efficiently identified the optimal parameters and effectively solved multiple-output problems.

Optimizing the design of straight bevel gear with reduced scoring effect

PurposeThe purpose of this paper is to minimize the volume of straight bevel gear and to develop resistance towards scoring failure in the straight bevel gear. Two evolutionary and more advance optimization techniques were used for performing optimization of straight bevel gears, which will also save computational time and will be less computationally expensive compared to a previously used optimization for design optimization of straight bevel gear.Design/methodology/approachThe following two different cases are considered for the study: the first mathematical model similar to that used earlier and without any modification to show efficiency of the optimization algorithm for straight bevel gear design optimization and the second mathematical model consist of constraints on scoring and contact ratio along with other generally used design constraints. Real coded genetic algorithm (RCGA) and accelerated particle swarm optimization (APSO) are used to optimize the straight bevel gear design. The effectiveness of the algorithms used has been validated by comparing the obtained results with previously published results.FindingsIt has been found that APSO and RCGA outperform other algorithms for straight bevel gear design. Optimized design values have reduced the scoring effect significantly. The values of the contact ratio obtained further enhances the meshing operation of the bevel gear drive by making it smoother and quieter.Originality/valueLow volume is one of the essential requirements of gearing applications. Scoring is a critical gear failure aspect that leads to the broken tooth in both high speed and low-speed applications of gears. The occurrence of scoring is hard to detect early and analyse. Scoring failure and contact ratio have been introduced as design constraints in the mathematical model. So, the mathematical model demonstrated in this paper minimizes the volume of the straight bevel gear drive, which has been very less attempted in previous studies, with scoring and contact ratio as some of the important design constraints, which the objective function has been subjected to. Also, two advanced and evolutionary optimization algorithms have been used to implement the mathematical model to reduce the computational time required to attain the optimal solution.

Optimization of Emission Waveform by Accelerated Particle Swarm Algorithm Based on Logarithmic Frequency Offset Mathematical Model

In this article, a novel technique is applied to achieve time-invariant spatial focusing performance for static and moving target by applying an irregular frequency modulation method to a frequency diverse array (FDA) waveform transmission system. The FDA radar can improve the anti-noise ability of the system by the means of concentrating the waveform energy on the required range or angle sector. However, the synthesized waveform has high sidelobes and is time-varying during the transmission waveform. In view of the above problems, a new waveform synthesis model of rang compensation frequency diverse array (TMRC-FDA) is established and the effect of parameter $$\varsigma$$ on the waveform synthesis is analyzed. A new accelerated particle swarm optimization algorithm is used to optimize the distribution of parameter $$\varsigma$$. The numerical simulation results show that the proposed TMRC-FDA waveform synthesis method achieves the goal of time invariance and reduces the waveform sidelobes. At the same time, it has better performance in terms of waveform focus compared to log-frequency offset FDA based on time-modulated and fixed parameters $$\varsigma$$.

Neural network modeling based double-population chaotic accelerated particle swarm optimization and diffusion theory for solubility prediction

Solubility is as a key chemical and physical property. Solubility prediction methods are applied in diverse fields including preparation synthesis and modifications of materials. To overcome the shortcomings of existing solubility prediction methods, taking the mass transfer of two-phase system as an example, a solubility prediction model based on the diffusion theory and hybrid artificial intelligence method was proposed in this paper. An improved double-population chaotic accelerated particle swarm optimization (APSO) algorithm combined diffusion theory was developed according to the particle evolution utilizing diffusion energy. The developed algorithm was applied in the training of parameters of the radial basis function artificial neural network and then a model for predicting solubility was developed. The experimental results of supercritical carbon dioxide solubility in 8 polymers were consistent with the predicted values by the model, indicating the high prediction accuracy. The average relative deviation, squared correlation coefficient, and root mean square error were respectively 0.0036, 0.9970, and 0.0152, displaying its higher comprehensive performance. The model may also be applied in other physicochemical fields.

Multi-Objective Optimal Robust Seat Suspension Control of Off-Road Vehicles in the Presence of Disturbance and Parametric Uncertainty Using Metaheuristics

Control of whole-body vibration (WBV) via a seat suspension in off-road vehicles is a challenging task due to the presence of severe external disturbances and parametric uncertainties. In this paper, a novel optimal robust (mixed ${\boldsymbol{H}_\infty }/{\boldsymbol{H}_2})$ controller is proposed to achieve enhanced vibration attenuation performance of seat suspensions considering the parametric uncertainties due to variations in driver mass and external disturbances encountered at the cabin floor including occasional shocks. A direct Lyapunov based LMI approach is employed to prove stability of the closed-loop system. In search of an optimal solution for the designed robust controller, a ${\boldsymbol{H}_\infty }$ disturbance attenuation performance together with the weighted ${\boldsymbol{H}_2}$ norm for minimizing the mean disturbance rejection, are considered using the Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO). The optimization problem for the proposed controller is formulated to minimize the frequency-weighted vibration dose value (VDV) due to acceleration response at the seat, while constraining the relative displacement between the seat and the seat base. The effectiveness of the proposed controller is illustrated through comparisons with performance achieved through some of the reported other seat suspension robust controllers in addition to a passive suspension. Results show that the proposed optimal robust controller could provide substantial reductions in the frequency-weighted acceleration at the seat and the VDV, measures of the force applied to the driver due to terrain induced vibration and shock, while limiting the relative displacement between the seat and the seat base.

Performance Analysis of APSO and Firefly Algorithm for Short Term Optimal Scheduling of Multi-Generation Hybrid Energy System

This paper presents a modified and novel form of the conventional short-term hydrothermal scheduling problem by incorporating the effects of adding the photovoltaic energy source to the conventional grid. A photovoltaic energy source is intermittent in nature, therefore, to determine the optimal power contribution of the photovoltaic source towards the economic dispatch problem, a detailed strategy is presented in this paper. The proposed design method includes the forecasting of the photovoltaic system’s parameters using the Auto-Regressive Integrated Moving Average (ARIMA) model. The analytical model is developed based on the fractional integral polynomials for studying the characteristics of the single photovoltaic module. The optimization of power allocation in the system consisting of conventional and non-conventional energy sources is highly non-linear and classical deterministic methods can not be guaranteed to determine the optimal solution for economic power dispatch. The global optima of such non-linear and non-convex problems can be determined using swarm-based intelligence techniques. This paper presents accelerated particle swarm optimization and the firefly algorithm to determine a solution for short-term non-linear scheduling problems. The multiple test cases have been presented to demonstrate the effectiveness of the proposed solution over classical methods. The overall generation cost of the selected hybrid system is reduced using the proposed methods while meeting the generation constraints of each energy source. Moreover, due to the stochastic nature of the meta-heuristic techniques, a comprehensive statistical comparison, based on the independent T-test results, is also presented to highlight the algorithm which performs better for selected scheduling problems. It has been demonstrated that accelerated particle swarm optimization gives lower mean generation cost of the system whereas the execution time of the firefly algorithm is better compared to its counterpart.

Interval Forecasting of Financial Time Series by Accelerated Particle Swarm-Optimized Multi-Output Machine Learning System

By providing a range of values rather than a point estimate, accurate interval forecasting is critical to the success of investment decisions in exchange rate markets. This work proposes a sliding-window metaheuristic optimization for interval-valued time series forecasting using multi-output least squares support vector regression (MLSSVR). The hyperparameters in MLSSVR are finetuned using an accelerated particle swarm optimization algorithm to yield the best predictions and fastest convergence. The proposed system has a graphical user interface that is developed in a computing environment and functions as a stand-alone application. The system is validated using stock prices as well as exchange rates and outputs are compared with published results. Finally, the proposed interval time series prediction method is tested in two case studies; one involves the daily Australian dollar and Japanese yen rates (AUD/JPY) and the other involves US dollar and Canadian dollar rates (USD/CAD). The proposed model is promising for interval time series forecasting.

A Hybrid Global Local Adaptive Particle Swarm Optimization-Based Support Vector Machine Model for Human Facial Authentication

A new facial authentication model called global local adaptive particle swarm optimization-based support vector machine, was proposed in this paper. The proposed model aimed to solve the problem of finding the preeminent parameters of support vector machine in order to come out with a powerful human facial authentication technique. The conventional particle swarm optimization algorithm was utilized with support vector machine to explore the preeminent parameters of support vector machine. However, the particle swarm optimization support vector machine model has some limitations in selecting the velocity coefficient and inertia weight. One of the best approaches, which is used to solve the velocity coefficient problem, is adaptive acceleration particle swarm optimization. Also, the global-local best inertia weight is used efficiently for selecting the inertia weight. Therefore, the global local adaptive particle swarm optimization-based support vector machine model was proposed based on combining adaptive acceleration particle swarm optimization, global-local best inertia weight, and support vector machine. The proposed model used the principal component analysis approach for feature extraction, as well as global local adaptive particle swarm optimization for finding the preeminent parameters of support vector machine. In the experiments, two datasets (YALEB and CASIAV5) were used, and the suggested model was compared with particle swarm optimization support vector machine and adaptive acceleration particle swarm optimization support vector machine methods. The comparison was via accuracy, computational time, and optimal parameters of support vector machine. Our model can be used for security applications and apply for human facial authentication.

Improving the performance of video Collaborative Filtering Recommender Systems using Optimization Algorithm

The growth amount of information on the Web makes it difficult for many web users to make decision and choose either information or goods. Thus, a recommender system is an approach that helps users to obtain their needs according to her/his preference within a massive amount of information rapidly without waste of time. The main advantage of using a recommender system in any online shopping or social media like Amazon, Netflix and Facebook is to increase the percentage of overall profits, customer satisfaction and retention. In this paper, we introduce an approach to increase the accuracy and to improve the performance of collaborative filtering recommender system. In this paper a hybrid approach is proposed to improve the performance of video collaborative filtering recommender system based on clustering and evolutionary algorithm. Proposed approach combines k-means clustering algorithm and two different evolutionary algorithms which are Accelerated Particle Swarm Optimization Algorithm (APSO) and Forest Optimization Algorithm (FOA). The main aim of this paper is to increase the accuracy of recommendation of user-based collaborative filtering video recommender system. Evaluation and computational results on the MovieLens dataset show that the proposed method has a better performance than the other related methods.

Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization

Nowadays, billions of Internet-of-Things devices generate various types of delay-sensitive tasks to process within a limited time frame. By processing the tasks at the network edge using distributed fog devices can efficiently overcome the deficiency of the centralized cloud data center (CDC), i.e., long latency and network congestion. Moreover, to overcome the inefficiency of the local fog devices, i.e., limited processing and storage capabilities, we investigate the collaboration between distributed fog devices and centralized CDC, where the delay-sensitive tasks can preferably be offloaded on the local fog devices, whereas the resource-intensive tasks are offloaded on the resource-rich CDC. However, one of the challenging tasks in the fog-cloud environment is to find a suitable computing device for each real-time task by considering tradeoff between the latency and cost. To meet the above-mentioned challenge, in this article, we introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique. The proposed APSO-based strategy finds an optimal computing device (i.e., fog device or cloud server) for each real-time task using multiple quality-of-service parameters, namely, cost and resource utilization (RU). The performance of the proposed algorithm is evaluated using four different real-time data sets with various performance matrices. The experimental results indicate that the proposed strategy outperforms the existing schemes in terms of average delay, computation time, RU, and average cost by 18%, 21%, 27%, and 23%, respectively.

Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm

As an important physical property of molecules, absorption energy can characterize the electronic property and structural information of molecules. Moreover, the accurate calculation of molecular absorption energies is highly valuable. Present linear and nonlinear methods hold low calculation accuracies due to great errors, especially irregular complicated molecular systems for structures. Thus, developing a prediction model for molecular absorption energies with enhanced accuracy, efficiency, and stability is highly beneficial. By combining deep learning and intelligence algorithms, we propose a prediction model based on the chaos-enhanced accelerated particle swarm optimization algorithm and deep artificial neural network (CAPSO BP DNN) that possesses a seven-layer 8-4-4-4-4-4-1 structure. Eight parameters related to molecular absorption energies are selected as inputs, such as a theoretical calculating value Ec of absorption energy (B3LYP/STO-3G), molecular electron number Ne, oscillator strength Os, number of double bonds Ndb, total number of atoms Na, number of hydrogen atoms Nh, number of carbon atoms Nc, and number of nitrogen atoms NN; and one parameter representing the molecular absorption energy is regarded as the output. A prediction experiment on organic molecular absorption energies indicates that CAPSO BP DNN exhibits a favourable predictive effect, accuracy, and correlation. The tested absolute average relative error, predicted root-mean-square error, and square correlation coefficient are 0.033, 0.0153, and 0.9957, respectively. Relative to other prediction models, the CAPSO BP DNN model exhibits a good comprehensive prediction performance and can provide references for other materials, chemistry and physics fields, such as nonlinear prediction of chemical and physical properties, QSAR/QAPR and chemical information modelling, etc.

APSO based LLRBFNN Model and Enhanced Fuzzy C Means algorithm for Brain Tumor Detection and Classification from Magnetic Resonance Image

This paper presents a novel APSO (Accelerated Particle Swarm Optimization) Predicated LLRBFNN (Local Linear Radial Basis Function Neural Network) model for automatic encephalon tumor detection and classification. The enhanced fuzzy c means algorithm (EnFCM) has been proposed for image segmentation and the GLCM (Gray Level Co-occurrence Matrix) technique for feature extraction from MR (Magnetic Resonance) images. This research work aims to utilize the hybrid models and algorithms for relegation and segmentation of encephalon tumors from the MR images. The extracted features have been alimented as input to the proposed APSO predicated LLRBFNN model for relegation of benign and malignant tumors. In this research work the proposed LLRBFNN model weights are optimized by utilizing APSO training which will provide unique solution to mitigation the hectic task of radiologist from manual detection of encephalon tumors from MR Images. Additionally the centers of the LLRBFNN model are culled by the Enhanced Fuzzy C Means algorithm and updated by the APSO algorithm. The results of proposed PSO predicated LLRBFNN model has been compared with PSO-LLRBFNN model, APSO-RBFNN and PSO-RBFNN model and the comparison results are presented. The experimental results obtained from the proposed model shows better relegation results as compared to the subsisting models proposed anteriorly.

Back-to-Back Competitive Learning Mechanism for Fuzzy Logic Based Supervisory Control System of Hybrid Electric Vehicles

This article proposes a novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs). This mechanism allows continuous competition between two fuzzy logic controllers during real-world driving. The leading controller will have the regulatory function of the supervisory control system. First, the configuration of the HEV model and its FL-based control system are analyzed. Second, the algorithm of chaos-enhanced accelerated particle swarm optimization (CAPSO) is developed for back-to-back learning of the membership function. Third, based on fuel-prioritized cost functions, the regulation of competitive assessment is designed to select a controller with a better fuel economy. Finally, the competitive performance of using the CAPSO algorithm is contrasted with other swarm-based methods and the BCLM-driven control system is validated by a hardware-in-the-loop test. The results demonstrate that the BCLM control system significantly reduces fuel consumption, at least 9% from charge sustaining and charge depleting based, and at least 7% from conventional FL-based systems.

Synthesis of Circular Array Antennas Using Accelerated Particle Swarm Optimization

Synthesis of Circular Array Antennas Using Accelerated Particle Swarm Optimization

Discontinuous grooves in thrust air bearings designed with CAPSO algorithm

Spiral groove thrust air bearings suffer from low load capacity. Although there have been studies on optimizing the groove geometry of spiral groove thrust air bearings to enhance the load capacity, all of them found so far have focused on continuous grooves only. Here, a novel groove optimization method, which allows the flexibility of generating either continuous or discontinuous grooves, is introduced for maximizing the load capacity of spiral groove thrust air bearings. In the proposed method, computational fluid dynamics is combined with the chaos-enhanced accelerated particle swarm optimization algorithm to search for the optimal groove geometry. A case study is given where the optimization of an initial spiral groove thrust air bearing is implemented. The optimized grooves are found to be discontinuous and of new geometry. Numerical analysis shows that the optimized discontinuous grooves can achieve higher load capacity than the optimized continuous grooves, the conventional herringbone, and the initial spiral grooves. The effect of the proposed method is further verified in experiments.

Inter‐harmonic parameters estimation in power grid based on accelerated PSO and T 5 R 11 window

A novel method on inter-harmonic parameters estimation is proposed. It contains two steps: pre-calculation and solution optimisation. An interpolated-discrete fourier transform (DFT)-based algorithm with a fifth-order-triangular and eleventh-order-rectangular (T5R11) window is applied in the pre-calculation process. Owing to mainlobe interferences caused by inter-harmonics in the frequency domain, the accelerated particle swarm optimisation (PSO) algorithm is proposed to optimise the solution of pre-calculation. Three aspects in the PSO are accelerated including utilising the especial DFT expression of T5R11 window, applying a changing inertia weight and locking the frequency. From the view of estimating accuracy, the errors caused by spectral leakage can be restrained to a degree of 10−11 by using T5R11 window, and the relative errors of parameters could be optimised to a degree of 10−5 at the tenth generation in the accelerated PSO. More simulations show that the proposed method can address many complex situations caused by inter-harmonics, and corresponding detection accuracies satisfy the requirements of national and international standards.