Version Of PSO Research Articles

An advanced electrical vehicle charging station using adaptive hybrid particle swarm optimization intended for renewable energy system for simultaneous distributions

A proposed hybrid approaches are incorporated in Electric Vehicle (EV) fast charging station (FCS) using (RES). Hybrid approach is improved by Adaptive Hybrid Particle Swarm Optimization (AHPSO) named as AHWPSO, moreover the proposed work Grey Wolf Optimization (GWO) is assist with adaptive hybridize PSO algorithm. Therefore, an overall pricing cost should be reduced maximum Electric Vehicle Charging Station (EVCS) with minimal installation. This simulation work is verified an adaptive time varying weightage parameters to increase the AHWPSO particle diversity factor. Proposed algorithm is incorporated with improve the novelty, and compared the results are recent version of PSO used for EVCS. Its increase the charging ability, energy loss minimization, voltage deviation reduction, and cost minimization. A distribution micro-grid capacity and demand are tested. Similarly, low to peak period energy variations are controlled by proposed algorithm with reduced capacitor bank. Overall control algorithm code is executed buy MATLAB/Simulink platform, the performance of this work listed, and compare to the existing approaches with achievement of maximum efficiency.

Cooperative Coevolutionary Particle Swarms using Fuzzy Logic for Large Scale Optimization

A cooperative coevolutionary framework can improve the performance of optimization algorithms on large-scale problems. In this paper, we propose a new Cooperative Coevolutionary Coevolutionary algorithm based on our preliminary work FuzzyPSO2. This new proposal, called CCFPSO, uses a variable decomposition method, adopting the random grouping technique and a dynamic subcomponent size at each generation. Unlike FuzzyPSO2, in CCFPSO the re-initialization of the variables suggested by the fuzzy system is performed on the particles that has the worst fitness value in each generation. Moreover, the particles are updated based on their best position and its neighborhoods, instead of the best particle in the population as its standard version. On high-dimensional problems that more closely resemble real-world problems (CEC2008, CEC2010) the performance of CCFPSO is favorable compared to other state-of-the-art PSO versions such as CCPSO2, SLPSO and CSO. The results indicate that using a Cooperative Coevolutionary PSO approach with a fuzzy logic system can improve results on high dimensionality problems (100 to 1000 variables).

Frequencies Wave Sound Particle Swarm Optimisation (FPSO)

ABSTRACT PSO is a remarkable tool for solving several optimisation problems, like global optimisation and many real-life problems. It generally explores global optimal solution via exploiting the particle – swarm’s memory. Its limited properties on objective function’s continuity along with the search space and its potentiality in adapting dynamic environment make the PSO an important meta-heuristic method. PSO has an inherent tendency of trapping at local optimum which affects the convergence prematurely, when trying to solve difficult problems. This work proposed a modified version of PSO called as FPSO, where frequency-wave-sound is employed to exit from any encountered local optimum; if it is not the optimal solution. This FPSO mimics the characteristics of the waves by using three parameters, namely amplitude, frequency and wavelength. FPSO is then compared and analysed with other renowned algorithms like conventional PSO, Grey Wolf Optimisation (GOW), Multi-Verse Optimiser (MVO), Moth-Flame Optimisation (SL-PSO), Sine Cosine Algorithm (PPSO) and Butterfly Optimisation Algorithm (BOA) on 23 bench marking test bed functions. The performance is evaluated using various measures including trajectory, search history, average fitness solution and best optimisation-solution. The obtained results show that the FPSO algorithm beats other metaheuristic algorithms and confirmed its better performance on 2-dimensional test functions.

Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter

This paper develops a full-bridge DC-AC converter, which uses a robust optimal tracking control strategy to procure a high-quality sine output waveshape even in the presence of unpredictable intermissions. The proposed strategy brings out the advantages of non-singular fast convergent terminal attractor (NFCTA) and chaos particle swarm optimization (CPSO). Compared with a typical TA, the NFCTA affords fast convergence within a limited time to the steady-state situation, and keeps away from the possibility of singularity through its sliding surface design. It is worth noting that once the NFCTA-controlled DC-AC converter encounters drastic changes in internal parameters or the influence of external non-linear loads, the trembling with low-control precision will occur and the aggravation of transient and steady-state performance yields. Although the traditional PSO algorithm has the characteristics of simple implementation and fast convergence, the search process lacks diversity and converges prematurely. So, it is impossible to deviate from the local extreme value, resulting in poor solution quality or search stagnation. Thereby, an improved version of traditional PSO called CPSO is used to discover global optimal NFCTA parameters, which can preclude precocious convergence to local solutions, mitigating the tremor as well as enhancing DC-AC converter performance. By using the proposed stable closed-loop full-bridge DC-AC converter with a hybrid strategy integrating NFCTA and CPSO, low total harmonic distortion (THD) output-voltage and fast dynamic load response are generated under nonlinear rectifier-type load situations and during sudden load changes, respectively. Simulation results are done by the Matlab/Simulink environment, and experimental results of a digital signal processor (DSP) controlled full-bridge DC-AC converter prototype confirm the usefulness of the proposed strategy.

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.

Tip relief designed to optimize contact fatigue life of spur gears using adapted PSO and Firefly algorithms

This paper examines the dynamic performances of circular profile modifications designed to optimize the contact fatigue life of spur gears. It combines the PSO and Firefly metaheuristics to a gear dynamic/degradation model. The objectives are to analyse the ability of optimal corrections to reduce dynamic loads and dynamic transmission error (DTE), and to describe the influence of the modification variables. To reduce computation efforts, the study modifies the original metaheuristics. In the proposed adaptation of the Firefly algorithm, the particle movement hinges on the brightest firefly perceived through the light-absorbing medium. This change reduces the number of function evaluations per iteration. The analysis shows that while the correction length is more influential, both modification amount and length alter the gear behavior, whereas the curvature radius influence remains modest. Curved corrections are more effective in ameliorating contact fatigue life, whereas larger curvature radii are better at reducing the DTE. Compared to the original gear set, the PSO and Firefly versions showed that optimized modifications engender substantial enhancements of the fatigue resistance. Moreover, optimal profiles also reduce both DTE and dynamic factors, but the inverse cannot be assumed.

A Multiple Remote Sensing Sensor Fusion System Using Choquet Fuzzy Integral and Modified Particle Swarm Optimization (FI-MPSO)

The simultaneous use of remote sensing sensors has been of great interest due to reasons such as the weaknesses of each sensor individually and the improvement in results by integrating multisensor information. In this context, decision fusion of multiremote sensing sensors is one of the hot research topics for improvement in land cover classification. This paper investigates a multisensor fusion system based on Choquet fuzzy integral and a modified particle swarm optimization for fusion of high-resolution visible RGB image and thermal infrared hyperspectral. Instead of using common optimization algorithms, a new version of PSO is used to reduce the running time and to increase the accuracy. After some processes and fuzzy classifications, a fuzzy integral–fuzzy measure fusion methodology which uses modified PSO to find the best subset of fuzzy measures is utilized to fuse the resulted decision profiles of soft decision-making system. Also, comparison between proposed method and other successful decision fusion techniques such as Dempsters shafer, AdaBoost and Bagging is investigated. Experiments are executed on high-resolution RGB image and thermal infrared hyperspectral data from Quebec of Canada. The results show that the suggested method obtains 92% of overall accuracy and outperforms the other decision fusion and optimization methods in classification performance.

IFODPSO-based multi-level image segmentation scheme aided with Masi entropy

This article presents an improved version of Fractional Order Darwinian PSO (IFODPSO) for segmenting 3D histogram-based color images at multiple levels of Berkley Segmentation Dataset (BSDS500). The success of convergence and accuracy rate of FODPSO algorithm depends on the value of fractional coefficient. This concept may provide drawback to the algorithm specially for multilevel problems of large dataset. So, to overcome the full dependency on fractional coefficient, delta potential model of quantum mechanics has been incorporated with FODPSO for updating the particle’s present as well as global position by destroying the worst particles (solutions), formulated using the introduction of the context parameter. Multi-level Massi Entropy (MME), of current interest, has been chosen here as the objective function for finding the threshold values in combination with IFODPSO. Further, the small segmented regions have been removed or merged into bigger regions for showing the better discrimination between different segmented objects. The effectiveness of the proposed MME-IFODPSO algorithm has been extensively investigated in terms of statistically and qualitatively in terms of the fidelity parameters with the state-of-art approaches and it has been found that the proposed method has improved at least 2–5% to the conventional methods in terms of accuracy.

Hybrid frameworks for flexible job shop scheduling

Effective scheduling is essential for manufacturing firms to survive in today’s fiercely competitive marketplace. Improvisation of schedule by simultaneous optimization of the performance measures is imperative for the manufacturing firms to stay ahead of competition and is one of the key responsibilities of shop floor managers. The current study addresses flexible job shop scheduling problem (FJSSP), considering three objectives: makespan (MS), maximal machine workload (MW), and total workload (TW). This research approaches the problem by developing two hybrid frameworks. The first framework (Hybrid-I) is a co-evolutionary combination of the social thinking capability of particle swarm optimization and local search capability of gravitational search algorithm. The second approach (Hybrid-II), hybridize the effectiveness of genetic algorithm in finding global best region with PSO’s cluster interactions to improve the search for an optimal solution. A well-designed, efficient version of PSO (ePSO), that inherits twofold improvement through variable random function strategy and mutation strategy, is applied in both the approaches. With the view to make a reasonable comparison between both approaches and with the state-of-the-art methods, tests have been conducted on 28 benchmark instances taken from three different data sets. Further, the managerial implication of this research has been validated by implementing Hybrid-II for an industrial case. The substantial performance of the proposed approach on benchmark instances as well as real-life industrial data supports a strong candidature for optimization of FJSSP.

Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO)

Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect.

A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine

Ground vibration is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, developing a precise model for prediction of ground vibration would be much beneficial to control environmental issues of blasting. The present study proposes a new hybrid machine learning (ML) technique, i.e., autonomous groups particles swarm optimization (AGPSO)–extreme learning machine (ELM) to predict ground vibration resulting from blasting. In fact, AGPSO–ELM model is a modified version of PSO–ELM that can solve problems in a way with higher prediction performance. For comparison purposes, PSO–ELM, minimax probability machine regression, least square–support vector machine and Gaussian process regression models were also proposed to estimate ground vibration. The said ML models were trained and tested based on a database comprising of 102 datasets collected from a quarry site in Malaysia. In the modeling of ML techniques, six input parameters were considered: burden to spacing ratio, maximum charge per delay, stemming, distance from the blasting-face, powder factor and hole depth. The results of ML techniques were evaluated in both stages of training and testing based on five fitness parameters criteria. Considering results of both training and testing datasets, AGPSO–ELM model was able to provide higher prediction performance for PPV prediction. Root-mean-square error values of (0.08 and 0.08) and coefficient of determination values of (0.92 and 0.90) were obtained, respectively, for training and testing datasets of AGPSO–ELM model which revealed that the new hybrid model is capable enough to forecast ground vibration induced by blasting. The newly proposed model can be used in other fields of science and engineering in order to get high accuracy level of prediction.

Development of PSO for tracking Maximum Power Point of Photovoltaic Systems

For a photovoltaic system, the relationship of the output voltage and power is usually non-linear, so it is essential to equip a MPPT controller in PV systems. Furthermore, the hotspot problem is a common phenomenon, resulting from the PV system operating under PSC. Partial shading not only damages the PV cells, but also makes it difficult to find the global MPP in the characteristic curves of P-V. The paper proposes a novel version of PSO, namely PPSO in order to detect the global peak among the multiple peaks, known as the true maximum energy from PV panel. For this, the PPSO algorithm makes the velocity of each particle be perturbed once the particles are struck into a local minima state in order to find the best optimum solution in the MPPT problem. The perturbation in the velocity vector of each particle not only helps them tracking the MPP accurately under the changing environmental conditions, such as large fluctuations of insolation and temperature like PSC; but also removes the steady-state oscillation. The proposed approach has been tested on a MPPT system, which controls a dc-dc boost converter connected in series with a resistive load. Moreover, the obtained results are compared to those obtained without any MPPT controller to prove the efficiency of the suggested method. In addition, this novel version gives the highest accuracy of tracking the optimum power in the least iteration number as compared to the conventional PSO.

Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem

PurposeIn the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.Design/methodology/approachA MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.FindingsThree test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.Originality/valueThe proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

Grid and Force Based Sensor Deployment Methods in Wireless Sensor Network using Particle Swarm Optimization

Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling & data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid & Force Based sensor deployment algorithms based on interval and packet size. From the results of Combination of Grid & Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.

Grid & Force Based Sensor Deployment Methods in WSN using PSO

<span>Wireless Sensor Network (WSN) is emergingtechnology and has wide range of applications, such as environment monitoring, industrial automation and numerous military applications. Hence, WSN is popular among researchers. WSN has several constraints such as restricted sensing range, communication range and limited battery capacity. These limitations bring issues such as coverage, connectivity, network lifetime and scheduling & data aggregation. There are mainly three strategies for solving coverage problems namely; force, grid and computational geometry based. PSO is a multidimensional optimization method inspired from the social behavior of birds called flocking. Basic version of PSO has the drawback of sometimes getting trapped in local optima as particles learn from each other and past solutions. This issue is solved by discrete version of PSO known as Modified Discrete Binary PSO (MDBPSO) as it uses probabilistic approach. This paper discusses performance analysis of random; grid based MDBPSO (Modified Discrete Binary Particle Swarm Optimization), Force Based VFCPSO and Combination of Grid & Force Based sensor deployment algorithms based on interval and packet size. </span><span>From the results of Combination of Grid & Force Based sensor deployment algorithm, it can be concluded that its performance is best for all parameters as compared to rest of the three methods when interval and packet size is varied.</span>

Fast and Effective Classification using Parallel and Multi-start PSO

PSO being a swarm based algorithm, can efficiently lend itself to operate on huge data. This article presents a technique that performs classification using PSO. An initial discussion is carried out describing PSO as a classifier. Three variants of PSO are proposed here; the first variant hybridizes PSO using Simulated Annealing and the next two variants parallelizes PSO. The two parallel variants of PSO are; Parallel PSO and Multistart PSO. Parallel PSO operates by parallelizing the operation of each of the particles and Multistart PSO runs several normal versions of PSO embedded with Simulated Annealing in parallel. The multi-start version is implemented to eliminate the problem of local optima. Experiments were conducted to identify the scalability and efficiency of PSO and its variants on huge and imbalanced data.

Accelerated micro particle swarm optimization for the solution of nonlinear model predictive control

Purpose Rapid solution methods are still a challenge for difficult optimization problems among them those arising in nonlinear model predictive control. The particle swarm optimization algorithm has shown its potential for the solution of some problems with an acceptable computation time. In this paper, we use an accelerated version of PSO for the solution of simple and multiobjective nonlinear MBPC for unmanned vehicles (mobile robots and quadcopter) for tracking trajectories and obstacle avoidance. The AµPSO-NMPC was applied to control a LEGO mobile robot for the tracking of a trajectory without and with obstacles avoidance one. Design/methodology/approach The accelerated PSO and the NMPC are used to control unmanned vehicles for tracking trajectories and obstacle avoidance. Findings The results of the experiments are very promising and show that AµPSO can be considered as an alternative to the classical solution methods. Originality/value The computation time is less than 0.02 ms using an Intel Core i7 with 8GB of RAM.

An aerial robot for rice farm quality inspection with type-2 fuzzy neural networks tuned by particle swarm optimization-sliding mode control hybrid algorithm

Abstract Agricultural robots, or agrobots, have been increasingly adopted in every aspect of farming from surveillance to fruit harvesting in order to improve the overall productivity over the last few decades. Motivated by the compelling growth of the agricultural robots in modern farms, in this work, an autonomous quality inspection over rice farms is proposed by employing quadcopters. Real-time control of these vehicles, however, is still challenging as they exhibit a highly nonlinear behavior especially for agile maneuvers. What is more, these vehicles have to operate under uncertain working conditions such as wind and gust disturbances as well as positioning errors caused by inertial measurement units and global positioning system. To handle these difficulties, as a model-free and learning control algorithm, type-2 fuzzy neural networks (T2-FNNs) are designed for the control of a quadcopter. The novel particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is proposed for the training of the T2-FNNs. In particular, the continuous version of PSO is adopted for the identification of the antecedent part of the T2-FNNs while the SMC-based update rules are utilized for the online learning of the consequent part during control. In the virtual environment, the quadcopter is expected to perform an autonomous flight including agile maneuvers such as steep turning and sudden altitude changes over a rice terrace farm in Longsheng, China. The simulation results for the T2-FNNs are compared with the outcome of conventional proportional-derivative (PD) controllers for different case studies. The results show that our method decreases the trajectory tracking integral squared error by %26 over PD controllers in the ideal case, while this ratio goes up to %95 under uncertain working conditions.

DPSO 알고리즘을 적용한 수동탐지소나 배치 연구

은밀하게 침투하여 아군의 핵심자산으로 접근하는 표적 잠수함을 상시 감시하기 위하여 수중 해저면 위치에 최적의 고정형 수동탐지소나를 배치하는 것을 고려 할 수 있다. 수동탐지소나 배치 최적화를 위한 효과도 지수는 넓은 탐지영역과 위치추정가능성의 함수로 적용할 수 있는데 계절적인 요인, 해상상태, 표적 잠수함의 침투심도 등의 다양한 확률적 변이를 포함하고 있어서 효과도지수가 배치의 입력에 대하여 확률적으로 나타나는 특성을 갖는다. 본 논문에서는 다양한 파라메타의 입력조건에 대하여 확률적인 출력을 갖는 수동탐지소나의 배치에 대한 최적화 문제를 정의하였으며, DPSO(Discrete binary version of PSO) 방법을 사용하여 최적 배치 안을 도출하기 위한 모의기반의 절차를 제시하고 고찰하였다. An arrangement of passive sonars is considered to be a fixed underwater surveillance system for detecting an anti-submarine consistently. An effectiveness score for optimizing the arrangement of passive sonars is defined in a function of the probability of detection and localization. These two features contain various probabilistic variations including seasons, sea states, depths of water, etc. Due to this reason, the effectiveness scores show probabilistic characteristics from the input of the arrangement of passive sonars. This paper defines the optimization problem having the results of probabilistic characteristics from various parameters of input conditions. Also, we suggest a simulation-based process of deciding the optimized arrangement of passive sonars using DPSO(Discrete binary version of PSO) method.

Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive Power Dispatch

Firefly algorithm (FA) is a newly proposed swarm intelligence optimization algorithm. The original version of FA usually traps into local optima like many other general swarm intelligence optimization algorithm. In order to overcome this drawback, the chaotic firefly algorithm(CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactive power optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PSO