PSO-based Approach Research Articles

Improved affinity propagation clustering algorithms: a PSO-based approach

Improved affinity propagation clustering algorithms: a PSO-based approach

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model

A PSO-based Approach to Determine the Market Clearing Price by Considering System Losses and Reactive Power Constraints in a Pool Market

A PSO-based Approach to Determine the Market Clearing Price by Considering System Losses and Reactive Power Constraints in a Pool Market

Radiometric Cross-Calibration of Wide-Field-of-View Cameras Based on Gaofen-1/6 Satellite Synergistic Observations Using Landsat-8 Operational Land Imager Images: A Solution for Off-Nadir Wide-Field-of-View Associated Problems

The Gaofen-1 satellite is equipped with four wide-field-of-view (WFV) instruments, enabling an impressive spatial resolution of 16 m and a combined swath exceeding 800 km. These WFV images have shown their valuable applications across diverse fields. However, achieving accurate radiometric calibration is an essential prerequisite for establishing reliable connections between satellite signals and biophysical, as well as biochemical, parameters. However, observations with large viewing angles (>20°) pose new challenges due to the bidirectional reflectance distribution function (BRDF) effect having a pronounced impact on the accuracy of cross-radiation calibrations, especially for the off-nadir WFV1 and WFV4 cameras. To overcome this challenge, a novel approach was introduced utilizing the combined observations from the Gaofen-1 and Gaofen-6 satellites, with Landsat-8 OLI serving as a reference sensor. The key advantage of this synergistic observation strategy is the ability to obtain a greater number of image pairs that closely resemble Landsat-8 OLI reference images in terms of geometry and observation dates. This increased availability of matching images ensures a more representative dataset of the observation geometry, enabling the derived calibration coefficients to be applicable across various sun–target–sensor geometries. Then, the geometry angles and bidirectional reflectance information were put into a Particle Swarm Optimization (PSO) algorithm incorporating radiative transfer modeling. This PSO-based approach formulates cross-calibration as an optimization problem, eliminating the reliance on complex BRDF models and satellite-based BRDF products that can be affected by cloud contamination. Extensive validation experiments involving satellite data and in situ measurements demonstrated an average uncertainty of less than eight percent for the proposed cross-radiation calibration scheme. Comparisons of top-of-atmosphere (TOA) results calibrated using our proposed scheme, the previous traditional radiative transfer modeling using MODIS BRDF products for BRDF correction (RTM-BRDF) method, and official coefficients reveal the superior accuracy of our method. The proposed scheme achieves a 36.99% decrease in root mean square error (RMSE) and a 38.13% increase in mean absolute error (MAE) compared to official coefficients. Moreover, it achieves comparable accuracy to the RTM-BRDF method while eliminating the need for MODIS BRDF products, with a decrease in RMSE exceeding 14% for the off-nadir WFV1 and WFV4 cameras. The results substantiate the efficacy of the proposed scheme in enhancing cross-calibration accuracy by improving image match-up selection, efficiently removing BRDF effects, and expanding applicability to diverse observation geometries.

A PSO-Based Approach for Optimal Allocation and Sizing of Resistive-Type SFCLs to Enhance the Transient Stability of Power Systems

Transient stability improvement of power systems in the event of short-circuit faults has always been an important issue in power systems analysis and studies. Resistive-type superconducting fault current limiters (RSFCL), owing to their capability in restricting fault currents, have been often taken into account as an efficient method to improve the transient stability of a power system. Regarding technical constraints as well as economic concerns, optimal allocation and sizing of RSFCLs in a power system play a crucial role in their efficient utilization. This paper aims to continue the authors’ previous work and enhance the transient stability of power systems by proposing an optimization approach for optimal sizing and the allocation of various candidate numbers of RSFCLs, as the most employed type of SFCL and the most efficient one in transient stability improvement. To solve the optimization problem, a PSO-based algorithm is solved in MATLAB through an objective function and related constraints. The efficacy of the proposed algorithm is evaluated by numerical studies on the IEEE 39-Bus New England system in various scenarios through the assessment of critical fault clearing time (CCT) as well as the generators rotor angle deviations as two crucial criteria for the transient stability of power systems. Simulating the optimization results in DIgSILENT Power Factory indicates an evident enhancement of the power system transient stability via employing optimized RSFCLs resulted from the proposed optimization algorithm. Moreover, the level of transient stability enhancement highly depends on the number of optimized RSFCLs employed in the power system. The results of this paper present a helpful guideline for power system planners to select an appropriate stability scheme based on RSFCLs besides other related technical and economic issues.

Two-Dimensional Gravity Inversion of Basement Relief for Geothermal Energy Potentials at the Harrat Rahat Volcanic Field, Saudi Arabia, Using Particle Swarm Optimization

We invert gravity and magnetic anomalies for basement relief at the Harrat Rahat Volcanic Field (HRVF) for the purpose of evaluating its geothermal energy prospects. HRVF is dominated by basaltic scoria cones and other volcanic rocks overlying the Proterozoic basement. The area considered for this study is located within the northern HRVF and consists mainly of alkali basalts with lesser amounts of benmoreite, mugearite, hawaiite, and trachyte. Our approach adopts a global optimization technique using Particle Swarm Optimization with automated parameter selection, and a two-dimensional gravity-magnetic (GM) forward modeling procedure. The results of the PSO-based approach indicate a depth to the basement at 0.10–624 m, with greater depths within the central region of a solitary anomalous density body in the HRVF. The obtained basement geometry is corroborated by the depth estimates obtained from other potential field inversion methods. The regions with higher prospects are mapped for a targeted future geothermal energy exploration at the HRVF, based on our inversion results.

Facility placement layout optimization

Optimal facility placement is key in field development planning. However, this process should be integrated with well placement and well trajectory design which poses a real challenge especially early in the field development planning; at the concept screening phase. During that phase, multiple concepts need to be assessed within, typically, a tight project time line. The integrated workflow becomes prohibitively expensive and calls, hence, for a fast and robust method that fulfils the objectives in the context of real onshore and offshore field development planning. Facility placement optimization consists mainly of minimizing the resulting cost while honouring topological complexities and prescribed capacity and trajectory constraints. It aims at selecting the optimal number and location of the different “nodes” comprising the facility and the optimal paths of the connections (pipelines) between nodes. Although many optimization schemes are developed and widely available in the literature, these methods are prohibitively slow with exhaustive search required, or they do not account for the various topological complexities typically encountered in real scenarios. Thus, the deficiency associated with these optimization schemes are drastically limiting in the case of the concept-select phase of field development planning where many scenarios need to be considered; in a relatively short timeframe. Deterministic optimization methods hit memory and computational limitations for all but trivial scenarios and, hence, fail to address the problem. Recently, genetic algorithms were applied with promising results addressing this challenge. In this paper, particle swarm optimization (PSO) is used for facility placement optimization and coupled with the A* algorithm for pipelines layout. This paper illustrates the capability of the implemented modular PSO-based approach for facility placement optimization through various levels of problem complexity: multiple facility layers, topological complexity and prohibited areas. The proposed algorithms are apt to be used in real field development planning projects and are, to be best of our knowledge, the first published material addressing the problem with the required level of speed, robustness and integration.

Optimal sizing of hybrid photovoltaic/diesel/battery nanogrid using a parallel multiobjective PSO-based approach: Application to desert camping in Hafr Al-Batin city in Saudi Arabia

Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid.

Elastic Particle Swarm Optimization for MarSCoDe Spectral Calibration on Tianwen-1 Mars Rover.

China's Tianwen-1 Mars rover carries a laser-induced breakdown spectroscopy (LIBS) payload named MarSCoDe to analyze the mineral and rock composition on Mars. MarSCoDe is expected to experience a wide working temperature range of about 100 °C, which will lead to a spectral shift of up to ∼40 pixels (∼8.13 nm). Even worse, drastic changes in temperature and environment may cause a loss or increase of some spectral lines of an on-board calibration Ti target. An elastic particle swarm optimization (PSO) approach is proposed to fulfill the on-board spectral calibration of MarSCoDe under this harsh condition. Through establishing a standard wavelength set (SWS) and an individual particle wavelength set (PWS), and further elastically selecting a part of PWS to compare with SWS, the problem of spectral shift and number mismatch can be solved gradually with the evolution of the particle swarm. Some tests of standard lamps and Ti with MarSCoDe, placed in a Mars simulation environment chamber (MSEC) in a temperature range of 70 °C, were completed. Compared with the standard spectrum of the Ti target (obtained at 20 °C), the spectral shifts of the first, second, and third channels are approximately 0.33 nm (5 pixels), 0.85 nm (6.4 pixels), and 8.09 nm (39.8 pixels), respectively, at -40 °C before correction; after PSO correction, the spectral shifts are greatly reduced to up to 0.015 nm, and specially for the 626.28 nm line, the spectral shift is reduced from 8.09 nm to about 0 nm. Experimental results demonstrate that the PSO-based approach can not only correct the on-board spectral shift but also solve the number mismatch of spectral lines of MarSCoDe in the harsh working environment of Mars. Further, it can be extended to the on-board calibration of other spectral payloads for deep space exploration.

SDPSO: Spark Distributed PSO-based approach for feature selection and cancer disease prognosis

The Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (PSO) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (PSO) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (BPSO) with Particle Swarm Optimization (PSO) algorithm for cancer prognosis is proposed; hence Spark Distributed Particle Swarm Optimization (SDPSO) approach. The effectiveness of the proposed approach is demonstrated using five benchmark genomic datasets as well as a comparative study with four state of the art methods. Compared with the four methods, the proposed approach yields the best in average of purity ranging from 0.78 to 0.97 and F-measure ranging from 0.75 to 0.96.

Intelligent UAV Deployment for a Disaster-Resilient Wireless Network.

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

Particle Swarm Optimization for Truck Scheduling Problem in Cross Docking Network

In cross docking network, multiple products from multiple origins are transferred by trucks through one or more cross docks. One critical concern is the decision on how to synchronise product transshipment through multiple cross docks to achieve timely shipment. This paper presents a mathematical model of truck scheduling problem in cross docking network in order to minimise makespan. Since the problem is NP-hard, a solution method is developed based on particle swarm optimisation (PSO) with two solution representations: randomised truck solution representation (Ra-SR) and prioritised truck solution representation (Pr-SR). The results show that the PSO-based approach performs well in solving the problem. Both solution representations are proven effective when comparing the solution quality and computational time with optimal results obtained from LINGO. However, the Pr-SR yields superior results to the Ra-SR in terms of solution quality and convergence behaviour for most instances especially in the case of large-size problems.

PSO for Fuzzy Clustering of Multi-view Relational Data

Particle Swarm Optimization (PSO) is a population-based meta-heuristic known for its simplicity, being successfully used in clustering task with interesting performance. Clustering of multi-view data sets has received increasing attention since it explores multiple sources or views of data sets aiming at improving clustering accuracy. Previous studies mainly focused on PSO-based clustering of single-view vector data, neither single- nor multi-view PSO-based clustering of relational received proper attention. This paper introduces a PSO-based approach to the fuzzy clustering of multi-view relational data, which can cluster data sets described by several dissimilarity matrices, each of them representing a particular view. In this work, ten fitness functions were considered, in which eight of them were adapted to deal with multi-view relational data and to consider the relevance weights of views. These fitness functions were compared to evaluate which best fit to cluster multi-view relational data. The performance and usefulness of the proposed approach, in comparison with previous single- and multi-view relational fuzzy clustering algorithms, are illustrated with several multi-view data sets. The Adjusted Rand Index (ARI) and F-measure were used to assess the quality of fuzzy partitions provided by clustering algorithms. The results have shown that the proposed methods significantly outperformed the compared algorithms in the majority of cases.

New highly efficient 2D SiC UV-absorbing material with plasmonic light trapping

The present paper is a systematic analysis of the thermoelectric and optical properties of the SiC monolayer. Based on the density functional theory (DFT) combined with the Boltzmann transport theory, the thermal conductivity, the electrical conductivity and the figures of merit are all determined and discussed for the SiC hybrid. At room temperature, it is found that SiC shows interesting values with respect to its counterparts graphene and silicene. To improve the absorption of the SiC sheet, a strategy is proposed using finite-difference time-domain (FDTD) combing with PSO-based approach. The absorbance of the UV-photodector with SiC monolayer and the SiC-based photodector with Au plasmonic grating are studied. Among our findings, the Au plasmonic grating enhances the absorbance of SiC to reach a maximum absorbance of 99.6% at the resonance wavelength of ( nm), which significantly improves the performance of UV-sensors. Therefore, by combining optical DFT analysis with FDTD simulation supported by global PSO optimization, we have been able to develop a new SiC monolayer high performance UV photodetector suitable for advanced optoelectronic applications.

PSO-Based Approach for Buckling Analysis of Shell Structures with Geometric Imperfections

The buckling loads of shell structures are sensitive to initial geometric imperfections. Conventional methods used to model geometric imperfections cannot determine the accuracy of buckling loads with high computational efficiency. A new computational approach based on particle swarm optimization (PSO) is proposed to obtain the lower bound of the buckling load of shell structures with geometric imperfections. The proposed approach assumes a nodal geometric position using uncertain parameters. The buckling loads of the shell structures are then optimized using the PSO-based approach. Both academic and practical numerical examples have been thoroughly investigated. Thus, the applicability and accuracy of the proposed method is critically validated.

Spectrum Allocation in Cognitive Radio: A PSO-based Approach

Cognitive radio systems have taken a fore-running position in the wireless communication technology. With most of the communication taking place through multi-carrier systems, the allocation of available spectrum to various carriers is a prominent issue. Since cognitive systems provide an environment of dynamic spectrum allocation, it becomes necessary to perform dynamic spectrum allocation swiftly with due consideration of parameters, like power consumption, fair distribution and minimal error. This paper considers a Particle Swarm Optimization-based approach, popularly used for solving large problems involving complex solution spaces to reach an optimal solution within feasible time. The mentioned spectrum allocation problem has been solved using PSO with a view to maximize the total transfer rate of the system, within specified constraints of maximum error rate, maximum power consumption and minimum transfer rate per user. The results have been compared with the existing Genetic Algorithm-based approach and have proved to be more effective.

High dimensional sentiment classification of product reviews using evolutionary computation

Feature selection is an important process in text classification. In general, traditional feature selection approaches are based on exhaustive search hence become inefficient due to a large search space. Further, this task becomes more challenging as the number of features increases. Recently, evolutionary computation (EC)-based search techniques have received a lot of attention in solving feature selection problem in high-dimensional feature space. This paper proposes a particle swarm optimisation (PSO)-based feature selection approach which is capable of generating the desired number of high-quality features from a large feature space. The proposed algorithm is tested on a large dataset and compared with several existing state-of-the-art algorithms used for feature selection. The accuracy of the underlying classifier has been considered as a measure of performance. Our obtained results demonstrated that the proposed PSO-based feature selection approach outperforms the other traditional feature selection algorithms in all the considered classifiers.

Real-Time Order Acceptance and Scheduling Problems in a Flow Shop Environment Using Hybrid GA-PSO Algorithm

With the emergence of new Industry 4.0 technologies, real-time order acceptance and scheduling is a key problem in a make-to-order (MTO) production system where customers place orders in real-time and the decision maker has to make acceptance or rejection decisions based on the available resources at that point in time. This paper focuses on simultaneously accepting orders and scheduling decisions in real-time, as is required for the operation of an MTO flow shop production system, a topic that has received little attention in academia due to the complexity of the problem. This paper presents a hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) to solve the considered problem. A detailed computational study based on realistic problem instances has been conducted. In this study, the hybrid GA- and PSO-based approach performed better than other state-of-the-art approaches reported in the literature.

Residual life prediction method for remanufacturing sucker rods based on magnetic memory testing and a support vector machine model

It is crucial for remanufacturability to be able to determine the residual life of remanufacturing cores. In the light of weaknesses in the available methods for fatigue damage evaluation and residual life prediction for remanufacturing sucker rods, a novel prediction approach using an optimised support vector machine (SVM) model based on metal magnetic memory (MMM) testing is proposed here. Firstly, tension-tension fatigue experiments on pre-cut groove sucker rod specimens are conducted to investigate the variations in the magnetic memory signals after different numbers of cycles and seven characteristic parameters are extracted to characterise the degree of fatigue damage. Then, a residual life prediction model for remanufacturing sucker rods based on a SVM model is established, where the SVM model parameters, including the radial basis function (RBF) kernel parameter and the penalty factor, are optimised in turn by a genetic algorithm (GA), partical swarm optimisation (PSO) and grid search optimisation (GSO). The results show that the proposed PSO-based approach significantly improves the prediction accuracy, compared with a basic SVM approach, and also yields more stable and accurate results when compared with the GA and the GSO method, providing a new and feasible approach for predicting remaining life.

A PSO-Based Approach for User-Pairing Schemes in NOMA Systems: Theory and Applications

This paper proposes an exhaustive analysis of a particle swarm optimization (PSO)-based configuration applied in non-orthogonal multiple access (NOMA) systems in order to perform user aggregation along different sub-channels. The idea behind this is to highlight the main characteristics of this PSO-based configuration for understanding how this policy enables the transmitter to require the minimum downlink transmitting power while guaranteeing the quality-of-service (QoS) constraint of each user. The analysis is carried out for two representative power-constrained scenarios, i.e., disaster relief network communications and unmanned aerial vehicle (UAV) communications, in which performing low-power transmissions represent an important aspect. Our results find applicability in the definition of explicit channel-state-aware strategies for user multiplexing in NOMA systems, which, at the date, represents a research field that remains to be investigated more in depth. Insightful discussions are provided from our analysis. For instance, depending on the number of available sub-channels and the channel gains experienced by each users along with the whole available bandwidth, it is shown how users must be multiplexed by the transmitter in order to reach the minimum transmitting power.