Advanced Particle Swarm Optimization Research Articles

Optimizing solar panel maximum power point tracking and parasitic parameter extraction in partial shading with Enhanced Slime Mold optimization

Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find near-optimal power points by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). The proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5 S–5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABSO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to the above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.

Optimization Analysis of the Shape and Position of a Submerged Breakwater for Improving Floating Body Stability

Submerged breakwaters can be installed underneath floating structures to reduce the external wave loads acting on the structure. The objective of this study was to establish an optimization analysis framework to determine the corresponding shape and position of the submerged breakwater that can minimize or maximize the external forces acting on the floating structure. A two-dimensional frequency-domain boundary element method (FD-BEM) based on the linear potential theory was developed to perform the hydrodynamic analysis. A metaheuristic algorithm, the advanced particle swarm optimization, was newly coupled to the FD-BEM to perform the optimization analysis. The optimization analysis process was performed by calling FD-BEM for each generation, performing a numerical analysis of the design variables of each particle, and updating the design variables using the collected results. The results of the optimization analysis showed that the height of the submerged breakwater has a significant effect on the surface piercing body and that there is a specific area and position with an optimal value. In this study, the optimal values of the shape and position of a single submerged breakwater were determined and analyzed so that the external force acting on a surface piercing body was minimum or maximum.

Evolutionary feature selection based on hybrid bald eagle search and particle swarm optimization

Feature selection is a complicated multi-objective optimization problem with aims at reaching to the best subset of features while remaining a high accuracy in the field of machine learning, which is considered to be a difficult task. In this paper, we design a fitness function to jointly optimize the classification accuracy and the selected features in the linear weighting manner. Then, we propose two hybrid meta-heuristic methods which are the hybrid basic bald eagle search-particle swarm optimization (HBBP) and hybrid chaos-based bald eagle search-particle swarm optimization (HCBP) that alleviate the drawbacks of bald eagle search (BES) by utilizing the advantages of particle swarm optimization (PSO) to efficiently optimize the designed fitness function. Specifically, HBBP is proposed to overcome the disadvantages of the originals (i.e., BES and PSO) and HCBP is proposed to further improve the performance of HBBP. Moreover, a binary optimization is utilized to effectively transfer the solution space from continuous to binary. To evaluate the effectiveness, 17 well-known data sets from the UCI repository are employed as well as a set of well-established algorithms from the literature are adopted to jointly confirm the effectiveness of the proposed methods in terms of fitness value, classification accuracy, computational time and selected features. The results support the superiority of the proposed hybrid methods against the basic optimizers and the comparative algorithms on the most tested data sets.

OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION

Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.

Controlling the significance of BLDC motor internal faults using dual examine algorithm in electric vehicle applications

<span lang="EN-US">This paper presents dual examine algorithm (DEA) to reduce residual error as well as to provide accurate phase currents without any distortion for a closed loop brushless direct current (BLDC) motor of an electric vehicle (EV). The underlying technology of DEA is a hybrid of the tabu search optimization (TSO) method and the genetic algorithm (GA). During closed loop operation of BLDC motor residual error is introduced by the discrepancy between the actual and reference speed, and the phase current distortion lowers the efficiency of the machine as a result machine performance is degraded. To address these issues, GA algorithm calculates the necessary parameters for the controller to produce precise current without distortion based on stator phase currents, and the suggested TSO algorithm limits the repeated operations in the PID controller to reduce the residual error to the greatest degree feasible. After primary examining, dual examine process initiate the transposing operation such as TSO is used to prove and calculate the phase current controller parameters, and GA is used to correct for remaining inaccuracy. To validate the proposed DEA algorithm is compared with advanced particle swarm optimization (APSO). The results verified the superiority of proposed DEA algorithm using MATLAB/Simulink platform.</span>

Spatial Channel Degrees of Freedom for Optimum Antenna Arrays

One of the ultimate goals of future wireless networks is to maximize data rates to accommodate bandwidth-hungry services and applications. Thus, extracting the maximum amount of information bits for given spatial constraints when designing wireless systems will be of great importance. In this paper, we present antenna array topologies that maximize the communication channel capacity for given number of array elements while occupying minimum space. Capacity is maximized via the development of an advanced particle swarm optimization (PSO) algorithm devising optimum standardized and arbitrarily-shaped antenna array topologies. Number of array elements and occupied space are informed by novel heuristic spatial degrees of freedom (SDoF) formulations which rigorously generalize existing SDoF formulas. Our generalized SDoF formulations rely on the differential entropy of three-dimensional (3D) angle of arrival (AOA) distributions and can associate the number of array elements and occupied space for any AOA distribution. The proposed analysis departs from novel closed-form spatial correlation functions (SCFs) of arbitrarily-positioned array elements for all classes of 3D multipath propagation channels, namely, isotropic, omnidirectional, and directional. Extensive simulation runs and comparisons with existing trivial solutions verify correctness of our SDoF formulations resulting in optimum antenna array topologies with maximum capacity performance and minimum space occupancy.

Machine Learning Based Design of Pattern Reconfigurable Antenna

In this study, a Machine Learning (ML) is implemented to soft computation of the Reconfigurable Horn Bowtie Dumbbell (RHBD) antenna at operating frequency range from 26 GHz to 29.5 GHz for 5G applications. An adaptive learning rate approach is used to build a ML model on a 5-layer system utilizing a simulated database of 180 RHBD antennas. In the training stage of a hybrid method that combines the advantages of particle swarm optimization (PSO) with a modified version of the gravitational search algorithm (MGSA), the architecture frame and hyper-parameters of the ML model are optimized. A precise electromagnetic analysis platform is used to simulate 180 RHBD antennas with varying geometrical properties in terms of the resonant frequency in order to create the database for training and testing the model. The ML model is tested and validated using a fabricated RHBD antenna operating at 27.5 GHz. Then, three PIN diodes are placed in the gaps of the reflectors located at the back of the antenna, and by changing the state of these PIN diodes, it can be noticed that they have a significant and direct effect on the radiation pattern, as they are able to change the beamwidth from 10.7° to 156.2°. The suggested antenna makes it easier to create dynamic radiation patterns that may be utilized to reconfigure the coverage area as required in accordance with the spatial-temporal user and traffic variations in high mobility environments.

Effect of Fuel Cells on Voltage Sag Mitigation in Power Grids Using Advanced Equilibrium Optimizer and Particle Swarm Optimization

Integration of Proton Exchange Membrane Fuel Cell (PEMFC) with electrical power grid (EPG) can improve the power quality (PQ) of EPG by injecting the required power. However, this makes the PQ issue more complicated due to the negative impact of voltage sag on EPG. Unfortunately, the classical P-I controllers fail in eliminating the voltage sag. In this context, this paper, attempts to mitigate the voltage sag in an interconnected PEMFC-EPG system by utilizing advanced equilibrium optimizer (AEO) and particle swarm optimization (PSO) controllers, and their efficiency is demonstrated by comparison with conventional P-I controllers. To achieve this goal, the AEO-PEMFC and PSO-PEMFC are employed in the EPG line with different fault scenarios. The obtained results unveil that both AEO-PEMFC and PSO-PEMFC provide the needed boost of voltage in the single line-to-ground faults (SLGF) scenario by 100.00%. For double line-to-ground faults (DLGF) scenario, a voltage boost of 99.56% and 98.39% is achieved while a voltage boost of 98.50% and 97.45% for the three line-to-ground faults (TLGF) scenario is obtained by the AEO-PEMFC and PSO-PEMFC, respectively.

A proposed framework for face - iris recognition system using enhanced mayfly algorithm

Fused biometrics systems have proven to solve some problems associated with unimodal systems but also face challenges in various aspects of their implementation such as difficulty in design, user acceptance is quite low, and the performance tradeoff. This framework tends to address some of these implementation challenges by using an enhanced mayfly algorithm, a modification of the existing mayfly algorithm that was recently proposed, as feature selection. Mayfly algorithm combines advantages of particle swarm optimization, genetic algorithm, and firefly algorithm, simulated in different experiments using varied benchmark function on conventional mayfly algorithm all tested to be capable of optimization, but despite its capabilities, some limitations such as slow convergent or premature convergent rate and possible imbalance between exploration and exploitation still remain unresolved, which necessitated enhancement for better performance. This framework will enhance the existing mayfly algorithm by expanding the search space which limited the ability of the conventional mayfly algorithm to be used to solve high-dimensional problem spaces such as feature selection and modify the selection procedure to model the attraction process as a deterministic process, that will be used for the feature selection procedure on fused face –iris recognition system. This will increase the capabilities of the mayfly algorithm and in turn, increase the recognition accuracy, and reduced the false acceptance rate, false rejection rate, and time complexity of the fused face–iris recognition system.

Optimization of biomimetic heliostat field using heuristic optimization algorithms

Central receiver systems are one of the most promising solar energy harvesting technologies. They consist of a large field of sun-tracking mirrors known as heliostats that focus sunlight onto a receiver at the top of a tower to generate high temperatures for running a heat cycle. Optimum localization of heliostats in the field plays an essential role during the design phase of a central receiver system. A good design aims to obtain the highest energy yield and efficiency at the lowest cost. The primary parameter that affects the energy yield of a central receiver system is the optical efficiency of its heliostat field. Optimization is carried out to obtain an arrangement of heliostats, which maximizes optical efficiency. This study presents the optimization process of a biomimetic heliostat field design using different heuristic optimization algorithms, namely advanced particle swarm optimization, genetic algorithm, whale optimization algorithm, and gravitational search algorithm. In un-optimized biomimetic heliostat fields, the efficiency is 66.4% which is enhanced to 68.7% after optimization. The results show that the advanced particle swarm is the fastest method that converges in less than 20 iterations. The instantaneous efficiency of the field increases by approximately 3.48% after optimization by particle swarm optimization, followed by 3% with the gravitational search algorithm, 2.88% from the whale optimization algorithm, and 1.99% with the genetic algorithm. The results are compared with other similar works in optimizing the biomimetic heliostat field.

An elitist multi-objective particle swarm optimization algorithm for composite structures design

Optimization is an important area of research in Engineering, usually due to the potentiality of saving costs and improving structural safety. Composite structures are typically complex, and the Finite Element Method is frequently required to evaluate such structures. From another perspective, Robust Design Optimization (RDO) is an approach that aims to consider the variability of the composite structures response due to uncertainty in design variables or material properties. Under these conditions, the problem of maximizing the robustness is added to the optimality problem related to minimizing the structure's weight. This work combines the advantages of Particle Swarm Optimization (PSO), such as simplicity and greater exploration and exploitation capabilities, with fitness assignment methodologies and elitist strategies commonly applied to Genetic Algorithms. The purpose is to achieve a more perceptible Pareto front and faster. The development is applied to the RDO bi-objective optimization problem in composite structures. Results for optimal design variables, critical displacements and stresses are discussed. The results show that elitist-based PSO approaches lead to a Pareto front with a larger number of optimal solutions, with more robust and lighter solutions when compared to other methodologies.

A back-diffusion median integrated evolutionary optimization algorithm

To accelerate the convergence speed and enhance robustness, a back-diffusion median integrated evolutionary algorithm (BMIEA) is proposed combining the advantages of particle swarm optimization (PSO) and differential evolution algorithm (DE) in this paper. The BMIEA includes three mainly optimization strategies. (1) Firstly, a new mean-median velocity updating formula is proposed to control optimal path of individuals. It can accelerate the convergence speed via reducing adverse effects of outliers on the population. (2) Secondly, a random differential mutation (RDM) inspired by the DE is devised to avoid the individuals trapping into local optimum via getting one more chance to explore optimal position while exploiting local region in each evolution. (3) Thirdly, a targeted exploration method i.e., back-diffusion operation, is proposed inspired by the duality principle to further accelerate the convergence rate and enhance robustness of algorithm. A series of simulation experiments have verified that BMIEA algorithm has revealed competitiveness compared with 13 state-of-art GOBL-based optimization algorithms.

Multiple-objective optimization of a reconfigurable assembly system via equipment selection and sequence planning

With the development of society and technology, the modern manufacturing system is required to handle unpredictable and rapidly changing market demands during various demand periods. This is a new challenge to the production scheduling of the production line, which requires a faster and smarter response to changes in production demand. To meet the challenge, the reconfigurable manufacturing system (RMS) has emerged as a competitive and promising manufacturing model. The reconfigurable assembly system (RAS), a critical feature of RMS, can be seamlessly incorporated into multi-variety, variable-batch, multi-functional, and fast-delivery manufacturing modes, opening up the possibility of rapid changes for production scheduling. In this paper, we first build a RAS model that can more accurately represent scheduling problems that may occur in real-world industrial operations. More specifically, we consider the assembly process of products could be divided into a number of major steps which are performed in different stations and optimize the assembly sequence of products and the equipment selection within individual assembly stations. After that, three objective functions corresponding to the workload balance, reconfiguration/assembly cost, and lead time are proposed to jointly optimize the assembly sequence and the equipment selection in individual assembly stations. Finally, a hybrid particle swarm optimization (MOHPSO) method is utilized to solve the multiple-objective optimization problem, which combines the advantages of particle swarm optimization and the hybrid method. Experimental results show the superiority of the proposed MOHPSO method over other alternatives including MOPSO and NSGA-II, achieving lower Spacing Metric (SM) and Mean Ideal Distance (MID) indexes and is suitable for solving production scheduling problems.

Efficiency Optimization Scheme for Isolated Triple Active Bridge DC–DC Converter With Full Soft-Switching and Minimized RMS Current

This article performs a comprehensive steady-state performance optimization for the isolated triple active bridge (TAB) dc–dc converter adopting the five-variable modulation (FVM) technique. First of all, a universal analytical model suitable for all the hardware conditions of the TAB converter is established using frequency-domain analysis method. Second, considering the influence of port voltages and the energy stored in parasitic capacitances, a simplified and accurate zero-voltage switching (ZVS) constraint is derived without complicated condition judgments. Based on this, aiming at reducing the overall system losses, a comprehensive efficiency optimization modulation scheme with FVM is proposed in this article. Distinct from the previous optimization methods, the proposed scheme is not only a multiobjective optimization with minimized circulating current level and full-ZVS operation. It is also a generic optimization scheme covering all the five operation modes, and there is no individual optimization for each mode, which greatly simplifies difficulty of implementation. Afterward, to overcome the cumbersome optimization process, an advanced particle swarm optimization algorithm is used to seek the global optimal operating point in the entire power flowing range. Extensive simulation is carried out using MATLAB/Simulink. And the steady-state characteristics of the proposed scheme are fully compared with the traditional phase shift modulation method. Finally, a 1.2 kW laboratory platform is built. A significant efficiency improvement under light-load condition up to 21% is achieved, which substantiates the feasibility of the proposed optimization scheme.

Adaptive multistrategy ensemble particle swarm optimization with Signal-to-Noise ratio distance metric

This paper proposes an adaptive multistrategy ensemble particle swarm optimization (PSO) with signal-to-noise ratio (SNR) distance metric called AMSEPSO, which aims to solve the problems of a single learning mode of PSO and easy premature convergence when solving complex problems. In AMSEPSO, an evolutionary state estimation (ESE) strategy selection framework is proposed based on the SNR distance metric, named ESE-SNR. The appropriate learning strategy is adaptively selected through the ESE-SNR framework. To balances diversity and convergence better, nonlinear acceleration coefficient based on Singer mapping is adopted. Finally, a global best perturbation mechanism is employed to help the population escape from the local optimum. On the CEC2017 benchmarks, comparison with other advanced PSO variants and meta-heuristic algorithms show that AMSEPSO achieves remarkable performance in solving functions with different characteristics, ranking first in the results. The results show that the ESE-SNR framework can effectively evaluate the search state of the population and can greatly save the computational time of the evaluation. The ESE-SNR framework proposed in this paper provides an innovative idea for the development of multistrategy ensemble learning, and the introduction of metric learning into the PSO community helps further promote the organic integration of machine learning and swarm intelligence.

Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm.

In order to improve the modeling performance of Volterra sequence for nonlinear neural activity, in this paper, a new optimization algorithm is proposed to identify Volterra sequence parameters. Algorithm combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA) improve the performance of the identification of nonlinear model parameters from rapidity and accuracy. In the modeling experiments of neural signal data generated by the neural computing model and clinical neural data set in this paper, the proposed algorithm shows its excellent potential in nonlinear neural activity modeling. Compared with PSO and GA, the algorithm can achieve less identification error, and better balance the convergence speed and identification error. Further, we explore the influence of algorithm parameters on identification efficiency, which provides possible guiding significance for parameter setting in practical application of the algorithm.

An advanced particle swarm optimization algorithm and its application to search flutter critical velocity of bridges

Flutter may lead to collapse of long-span bridges and need to be avoided, which makes the estimation of the onset flutter essential for long-span bridge design. However, the efficiency of traditional solution methods of predicting onset flutter is not high. In this study, it is converted into an optimization model, which is solved by a proposed advanced particle swarm optimization (APSO) with a novel inertia weight strategy and mutation mechanism. Two well-known benchmark functions are firstly employed to validate the performance of APSO. Compared with other existing PSO methods, APSO not only has high accuracy, but also has the fastest convergence speed. Then, APSO is used to find the flutter critical wind speed of bridges. A good agreement is obtained comparing predictions on the onset flutter by the proposed methods and available tests on several study cases. In addition, the value range of APSO variables is discussed in depth. It is confirmed that the proposed APSO algorithm has excellent stability, robustness and fast convergence, which is suitable for searching onset flutter of bridges.

A Composite Particle Swarm Optimization Algorithm for Hospital Equipment Management Risk Control Optimization and Prediction.

Aiming at the problem that particles cannot realize multidimensional analysis and poor global search ability, a composite particle swarm optimization algorithm is proposed, improving the accuracy of particle swarm optimization. Firstly, k-clustering is used to cluster risk management particle swarm optimization. The advantages of particle swarm optimization have to be given full play, and the risk of hospital equipment management from various aspects has to be controlled. Then, the multidimensional particle swarm is segmented to obtain an ordered multidimensional risk particle swarm set, which provides a basis for later risk prediction. Finally, through the fusion function of multidimensional risk particle swarm, the risk particle swarm set based on the clustering degree is constructed, and the optimal extreme value is obtained, so as to improve the accuracy of management risk calculation results. Through MATLAB simulation analysis, it can be seen that the composite particle swarm optimization algorithm is better than particle swarm optimization algorithm in global search accuracy and search time. Moreover, the calculation time and accuracy are better. Therefore, the composite particle swarm optimization algorithm can be used to analyze the risk of hospital equipment and effectively control the risk of hospital equipment management.

Cloud-based neuro-fuzzy hydro-climatic model for water quality assessment under uncertainty and sensitivity.

River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When data is deficient and noisy, it creates missing and conflicting parameters within their complex inter-relationships. It is also essential to model how climatic variations and river discharge affect water quality. The present study proposes a cloud-based efficient and resourceful machine learning (ML) modeling framework using an artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and advanced particle swarm optimization (PSO). The framework assesses the sensitivity of five critical water quality parameters namely biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, temperature, and total coliform toward WQI of the River Ganges in India. Monthly datasets of these parameters, river flow, and climate components (rainfall and temperature) for a nine-year (2011-2019) period have been used to build the models. We also propose collecting the data by placing various monitoring sensors in the river and sending the data to the cloud for analysis. This helps in continuous monitoring and analysis. Results indicate that ANN and ANFIS capture the nonlinearity in the relationship among water quality parameters with a root mean square error (RMSE) of 7.5 × 10-7 (0.002%) and 1.02 × 10-5 (0.029%), respectively, while the combined ANN-PSO model gives normalized mean square error (NMSE) of 0.0024. The study demonstrates the role of cloud-based machine learning in developing watershed protection and restoration strategies by analyzing the sensitivity of individual water quality parameters while predicting water quality under changing climate and river discharge.

Online Voltage Control of DC Microgrids with Different Loading Conditions Using Particle Swarm Optimization

This paper presents a contemplate study on online voltage control strategy with different loading conditions in a DC microgrid using particle swarm optimization (PSO). DC microgrids consist of many converters and it is an important issue to stabilize network voltage by properly designing their controllers. Conventional feedback controls have been often used as a primary control for voltage stabilization with optimizing control parameters such as droop gains. Moreover, the optimal control parameters should be flexibly updated when the system condition dynamically changes so as to maintain high voltage control performance under sudden and unexpected changes in system conditions. In that context, this paper introduces an online voltage control using an advanced PSO technique to frequently update control gains. The primary control was modeled as a proportional‐derivative controller, and the optimization problem was defined based on the eigenvalue analysis. A parallel operation of local and global searches is conducted in the proposed method to search optimal control gains by allocating a part of particles to move in a random pattern. The validation of the proposed method was performed using a simplified 3‐bus DC microgrid model with two generator sets and two constant power loads. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.