Multi-swarm Particle Swarm Optimization Algorithm Research Articles

A new MPPT controller based on a modified multiswarm PSO algorithm using an adaptive factor selection strategy for partially shaded PV systems

Maximum power point tracking (MPPT) controller is the main element in photovoltaic (PV) systems, which is used to ensure maximum power extraction under different meteorological conditions. A MPPT controller can guarantee good performance criteria even in the presence of climatic changes. To achieve this goal, several techniques have been proposed in the literature to improve robustness of the PV system control, such as artificial intelligence and multiswarm particle swarm optimization (MSPSO) algorithm. Previous research on classical MSPSO has shown that the algorithm search behavior cannot find the optimal solution for certain problems. In this context, we investigate the design of a new MPPT controller based on a modified version of heterogeneous multiswarm particle swarm optimization algorithm using an adaptive factor selection strategy (FMSPSO) for PV systems. The proposed FMSPSO can improve the tracking capability with high accuracy, less oscillations, and high robustness. To validate the proposed solution, a simulation and experimental benchmarking of a PV system are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared with the classical MSPSO, fuzzy logic, and perturb and observe (P&O) control presented in other recent works.

Real-time implementation of a novel MPPT control based on the improved PSO algorithm using an adaptive factor selection strategy for photovoltaic systems

Particle Swarm Optimization (PSO) is considered as one of Maximum Power Point Tracking (MPPT) controller algorithm developed for PhotoVoltaic system (PV) to guarantee a maximum power extraction under different climatic conditions of temperature and irradiation. Classical MPPT algorithms like Perturbe and Observe (P&O), PSO, Adaptive Neuro-Fuzzy Inference System (ANFIS) are an effective method for tracking the maximum power point (MPP) for the PV systems, but the problems with these approaches that they are less stable, high oscillation around steady state and slower convergence to the MPP. Based on recent research, the purpose of this paper is to introduces a novel MPPT controller based on a modified version of heterogeneous multi swarm PSO algorithm using an adaptive factor selection strategy (FMSPSO), to overcome the previous shortcomings and compared with conventional PSO, ANFIS and classical P&O controllers. Simulation and experimental results revealed that the new FMSPSO algorithm can overcome the previous shortcomings providing the superior performance to track the MPP efficiently with a shorter convergence time and small oscillations compared to other algorithms. The experimental confirmation of the FMSPSO algorithm has been carried out using NI-myRIO-1900 card and shows that with the proposed MPPT approach efficiency can reach a value greater than 99% even in climatic variation of irradiation and temperature.

Electrochemical promoted dry methane reforming for power and syngas co-generation in solid oxide fuel cells: Experiments, modelling and optimizations

The solid oxide fuel cell (SOFC) combining dry methane reforming (DMR) is an efficient electrochemical power generation device that simultaneously converts greenhouse gases (methane and CO2) into syngas and produces electricity power. The electrochemical promotion of catalysis effect (EPOC) in SOFC is known to be promising for enhancing the syngas conversion e.g. dry methane reforming reaction upon application of electrical currents or potentials. However, traditional DMR catalytic kinetic models were developed from heterogeneous catalysis experimental data, neglecting the EPOC effect and thus fail to accurately predict the DMR catalytic kinetics in SOFC. This study experimentally investigated the EPOC effect on the DMR reaction during SOFC operation, and proposes a machine learning-based predictive model using multiswarm particle swarm optimization algorithm (MSPSO) and back propagating (BP) neural network for the accurate prediction of catalysis performance in DMR-SOFCs under the EPOC. Key parameters including molar flow rate, reaction temperature, and electrical potentials are used as input parameters and CH4/CO2 conversion as output in the predictive model. The MSPSO-BP model exhibits high prediction accuracy with the average error of predicted CH4/CO2 conversion less than 5 %, and the coefficient of determination (R2) values are 0.971 and 0.968. respectively. Sensitivity analysis through the response surface method (RSM) reveals that temperature and electrical potentials are the most important parameters affecting dry methane reforming performance under EPOC. The developed model in this work is the first machine learning-based predictive model for DMR-SOFCs with a focus on EPOC effect and co-generation performance, providing a valuable tool for the optimization and design of future efficient DMR-SOFCs systems.

MISPSO-Attack: An efficient adversarial watermarking attack based on multiple initial solution particle swarm optimization

The vulnerability of deep learning models to adversarial attacks is a growing concern, as the emergence of adversarial samples exposes almost all models to the risk of such attacks. This paper proposes a new method for adversarial attacks through watermarking. Our goal is to leverage the properties of adversarial samples to prevent people’s images from being maliciously collected and compared, thereby avoiding the leakage of private information. Our method, which improves on the multi-swarm particle swarm optimization (MPSO) algorithm, outperforms existing similar methods on two popular computer vision datasets. We conducted attack experiments on the widely used Imagenet dataset and achieve the highest attack success rate of 89.50%. The experimental results demonstrate the superiority of our method over existing similar methods. We simulate the attacks on the online social environment using two face photographs datasets and face recognition models. Our method reaches the best deception performance compared to similar methods, with the highest success rate of 97.03%, demonstrating our approach’s ability to protect individuals’ privacy. Furthermore, we investigate the natural causes of adversarial samples and demonstrate their inevitability, providing valuable insights for developing more robust deep models. The source code of the proposed method is available online at: https://github.com/grandwang/main_attack.

Cooperative path planning optimization for multiple UAVs with communication constraints

Path planning is a complicated optimization problem that is crucial for the safe flight of unmanned aerial vehicles (UAVs). Especially in the scenarios involving multiple UAVs, this problem is highly challenging due to the constraints of complex environments, various tasks and inherent UAV maneuverability. In this paper, a cooperative path planning model for multiple UAVs is presented. In addition to common limitations such as the path length minimization, UAV maneuverability limitation and collision avoidance, the communication requirements between UAVs and the impact of obstacles in the flight environment on the quality of communication are also taken into account in the presented path planning model. On this basis, the corresponding objective function is designed. Then, an improved particle swarm optimization (PSO) algorithm is proposed to solve the above path planning problem. Utilizing the ideas of the dynamic multi-swarm PSO (DMSPSO) algorithm and the comprehensive learning PSO (CLPSO) algorithm, the proposed algorithm, denoted as CL-DMSPSO, further improves the performance of both algorithms. The effectiveness and superiority of the novel CL-DMSPSO algorithm is verified on benchmark functions, especially for complex multimodal functions. Finally, we present an effective path planning method using CL-DMSPSO to generate optimized flyable paths for multiple UAVs. And simulation and comparison results on the designed scenario indicate the proposed UAV path planning method can efficiently plan high-quality paths for UAVs and demonstrate the advantages of the proposed CL-DMSPSO algorithm compared with other PSO algorithms in UAV path planning.

Research on Particle Swarm Compensation Method for Subdivision Error Optimization of Photoelectric Encoder Based on Parallel Iteration.

Photoelectric encoders are widely used in high-precision measurement fields such as industry and aerospace because of their high precision and reliability. In order to improve the subdivision accuracy of moiré grating signals, a particle swarm optimization compensation model for grating the subdivision error of a photoelectric encoder based on parallel iteration is proposed. In the paper, an adaptive subdivision method of a particle swarm search domain based on the honeycomb structure is proposed, and a raster signal subdivision error compensation model based on the multi-swarm particle swarm optimization algorithm based on parallel iteration is established. The optimization algorithm can effectively improve the convergence speed and system accuracy of traditional particle swarm optimization. Finally, according to the subdivision error compensation algorithm, the subdivision error of the grating system caused by the sinusoidal error in the system is quickly corrected by taking advantage of the high-speed parallel processing of the FPGA pipeline architecture. The design experiment uses a 25-bit photoelectric encoder to verify the subdivision error algorithm. The experimental results show that the actual dynamic subdivision error can be reduced to ½ before compensation, and the static subdivision error can be reduced from 1.264″ to 0.487″ before detection.

A Dynamic Multi-Swarm Particle Swarm Optimization With Global Detection Mechanism

To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.

Co-Evolutionary Multi-Swarm PSO Based Optimal Placement of Miscellaneous DGs in a Real Electricity Grids Regarding Uncertainties

Distributed generators (DGs) facilitate minimizing a monetary objective for controlling overload or low-voltage obstacles. In conjunction with controlling such complications, a DG unit can be allocated for maximum reliability or efficiency. This study presents a new method based on a new index for locating and sizing DGs in electricity distribution systems. Stable node voltages which are known as power stability index (PSI) are considered in developing the index. An analytical method is applied in visualizing the effect of DG on losses, voltage profile, and voltage stability of the system. In this study, a new approach using co-evolutionary multi-swarm particle swarm optimization (CMPSO) algorithm is purposed for locating DGs in radial electrical distribution systems considering the uncertainty of solar power as well as load and wind power. In this paper, the optimal locations and sizes of DG units are calculated by considering the active power loss, reliability index, and PSI as objective functions. The presented algorithm is tested on 33-bus and 274-bus real distribution networks. The results of the simulation show the effectiveness of the proposed method.

An Expanded Heterogeneous Particle Swarm Optimization Based on Adaptive Inertia Weight

In this study, a modified version of multiswarm particle swarm optimization algorithm (MsPSO) is proposed. However, the classical MsPSO algorithm causes premature stagnation due to the limitation of particle diversity; as a result, it is simple to slip into a local optimum. To overcome the above feebleness, this work presents a heterogeneous multiswarm PSO algorithm based on adaptive inertia weight strategies called (A-MsPSO). The MsPSO’s main advantages are that it is simple to use and that there are few settings to alter. In the MsPSO method, the inertia weight is a key parameter affecting considerably convergence, exploration, and exploitation. In this manuscript, an adaptive inertia weight is adopted to ameliorate the global search ability of the classical MsPSO algorithm. Its performance is based on exploration, which is defined as an algorithm’s capacity to search through a variety of search spaces. It also aids in determining the best ideal capability for searching a small region and determining the candidate answer. If a swarm discovers a global best location during iterations, the inertia weight is increased, and exploration in that direction is enhanced. The standard tests and indications provided in the specialized literature are used to show the efficiency of the proposed algorithm. Furthermore, findings of comparisons between A-MsPSO and six other common PSO algorithms show that our proposal has a highly promising performance for handling various types of optimization problems, leading to both greater solution accuracy and more efficient solution times.

A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach. In order to bring about a balance between the exploration and exploitation characteristics of MSPSO allowing to promote information exchange amongst the subswarms. However, the classical MSPSO algorithm search behavior has not always been optimal in finding the optimal solution to certain problems, which results in falling into local optimum leading to premature convergence. The most advantages of the MSPSO there are easy to implement and there are few parameters to adjust. The inertia weight (w) is one of the most Particle Swarm Optimization’s (PSO) parameters. Controlling this parameter could facilitate the convergence and prevent an explosion of the swarm. To overcome the above limitations, this paper proposes a heterogeneous multi swarm PSO algorithm based on PSO number selection approach centred on the idea of particle swarm referred to as Multi-Swarm Particle Swarm Optimization algorithm with Factor selection strategy (FMSPSO). In the various process implementations of the particle swarm search, different parameter selection strategies are adopted to ameliorate the global search ability. The proposed FMSPSO is able to improve the population’s diversity and better explore the entire feature space. The statistical test and indicators that are reported in the specialized literature demonstrate that the suggested approach is superior in terms of efficiency to nine other popular PSO algorithms in solving the optimization problem of complex problems. The approach suggests that FMSPSO reaches a very promising performance for solving different types of optimization problems, leading eventually to higher solution accuracy.

UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

This paper presents the method to solve the problem of path planning for an unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and line-of-sight (LOS) calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS), and dynamic constraints of UAV are considered to determine the cost of the path. An improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. The proposed algorithm is composed of preprocessing steps, multi-swarm PSO algorithm, and postprocessing steps. The Voronoi diagram and Dijkstra algorithm as preprocessing steps provide the initial path for the multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Postprocessing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation, and the graphic processing unit (GPU) implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms, and the number of cost evaluation points. The t -test results show that the suggested method is statistically better than existing methods.

Mutual-inclusive learning-based multi-swarm PSO algorithm for image segmentation using an innovative objective function

This paper presents a novel image segmentation algorithm formed by the normalised index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a high dimensional problem of particle swarm optimisation (PSO) is divided into several one-dimensional problems to get rid of the 'high dimensionality' problem whereas premature convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images which produce better optimal thresholds at a faster convergence rate with high functional values as compared to the considered optimisation techniques like PSO, genetic algorithm (GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity parameters of the proposed algorithm is carried out over the other stated global optimisers.

Mutual-inclusive learning-based multi-swarm PSO algorithm for image segmentation using an innovative objective function

This paper presents a novel image segmentation algorithm formed by the normalised index value (Niv) and probability (Pr) of pixel intensities. To reduce the computational complexity, a mutual-inclusive learning-based optimisation strategy, named mutual-inclusive multi-swarm particle swarm optimisation (MIMPSO) is also proposed. In mutual learning, a high dimensional problem of particle swarm optimisation (PSO) is divided into several one-dimensional problems to get rid of the 'high dimensionality' problem whereas premature convergence is removed by the inclusive-learning approach. The proposed Niv and Pr-based technique with the MIMPSO algorithm is applied on the Berkley Dataset (BSDS300) images which produce better optimal thresholds at a faster convergence rate with high functional values as compared to the considered optimisation techniques like PSO, genetic algorithm (GA) and artificial bee colony (ABC). The overall performance in terms of the fidelity parameters of the proposed algorithm is carried out over the other stated global optimisers.

A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting

This paper proposes a multi-swarm particle swarm optimization (MSPSO) that consists of three novel strategies to balance the exploration and exploitation abilities. The new proposed MSPSO in this work is based on multiple swarms framework cooperating with the dynamic sub-swarm number strategy (DNS), sub-swarm regrouping strategy (SRS), and purposeful detecting strategy (PDS). Firstly, the DNS divides the entire population into many sub-swarms in the early stage and periodically reduces the number of sub-swarms (i.e., increase the size of each sub-swarm) along with the evolutionary process. This is helpful for balancing the exploration ability early and the exploitation ability late, respectively. Secondly, in each DNS period with special number of sub-swarms, the SRS is to regroup these sub-swarms based on the stagnancy information of the global best position. This is helpful for diffusing and sharing the search information among different sub-swarms to enhance the exploitation ability. Thirdly, the PDS is relying on some historical information of the search process to detect whether the population has been trapped into a potential local optimum, so as to help the population jump out of the current local optimum for better exploration ability. The comparisons among MSPSO and other 13 peer algorithms on the CEC2013 test suite and 4 real applications suggest that MSPSO is a very reliable and highly competitive optimization algorithm for solving different types of functions. Furthermore, the extensive experimental results illustrate the effectiveness and efficiency of the three proposed strategies used in MSPSO.

Appliances scheduling via cooperative multi-swarm PSO under day-ahead prices and photovoltaic generation

This work studies the problem of appliances scheduling in a residential unit. An appliance-scheduling model for the home energy management system (HEMS) is established based on day-ahead electricity prices and photovoltaic (PV) generation. The HEMS receives the meter data and calculates the scheduling strategies, then the HEMS sends control signals to achieve the on/off control of the appliances through the ZigBee (a wireless communication technology with low power consumption in short distance). The study starts with a view to minimizing the summation of the electricity payments, the consumer's dissatisfaction (DS), and the carbon dioxide emissions (CDE), and the constraints specify the restrictions on the operating time and the power consumption of the appliances. A cooperative multi-swarm particle swarm optimization (PSO) algorithm is adopted to solve the combinational optimization problem. The appliances can be categorized into shiftable and non-shiftable appliances. For the shiftable appliances, the start time and power of the appliances can be scheduled flexibly in the case of the announced electricity prices. Furthermore, the plug-in hybrid electric vehicle (PHEV) is introduced to charge or discharge for energy management. Specially, the ability of selling electricity (SE) to the power grid is studied for appliances scheduling. Finally, the simulation results demonstrate that the cooperative multi-swarm PSO algorithm shows good convergence performance under different scenarios. Moreover, The electricity payments can be reduced by considering the carbon dioxide emissions in the objective function and selling electricity to the power grid, which also achieves the peak load curtailment.

Optimal capacitor placement in distorted distribution systems considering resonance constraint using multi‐swarm particle swarm optimisation algorithm

Optimal capacitor placement in distribution systems has been studied for many reasons such as the improvement of voltage profile, mitigation of total harmonic distortion, and reduction of power losses. In this study, a new approach is presented for capacitor placement in the presence of harmonic distortion along with a proposed resonance index (RI). In this approach, a sensitivity analysis is first applied to find candidate buses. After that, the size and location of capacitors are optimised by a proposed fitness function. The fitness function includes a fuzzy membership function of annual net benefit, maximum THD of voltage, maximum voltage deviation, and a resonance constraint. This procedure supports different load levels employing fixed and switched capacitors. Also, a new RI is presented to prevent resonance conditions. Finally, the approach is tested on an IEEE 18-bus and an IEEE 69-bus system using the multi-swarm particle swarm optimisation algorithm. The simulation results show the efficiency of the method in comparison with the other methods.

An improved heterogeneous multi-swarm PSO algorithm to generate an optimal T-S fuzzy model of a hydraulic process

In this paper, a new methodology to develop an Optimal Fuzzy model (OptiFel) using an improved Multi-swarm Particle Swarm Optimization (MsPSO) algorithm is proposed with a new adaptive inertia weight based on Grey relational analysis. Since the classical MsPSO suffers from premature convergence and can be trapped into local optima, which significantly affects the model accuracy, a modified MsPSO algorithm is presented here. The most important advantage of the proposed algorithm is the adjustment of fewer parameters in which the main parameter is the inertia weight. In fact, the control of this parameter could facilitate the convergence and prevent an explosion of the swarm. The performance of the proposed algorithm is evaluated by adopting standard tests and indicators which are reported in the specialized literature. The proposed Grey MsPSO is first applied to solve the optimization problems of six benchmark functions and then, compared with the other nine variants of particle swarm optimization. In order to demonstrate the higher search performance of the proposed algorithm, the comparison is then made via two performance tests such as the standard deviation and central processing unit time. To further validate the generalization ability of the Improved OptiFel approach, the proposed algorithm is secondly applied on the Box–Jenkins Gas Furnace system and on a irrigation station prototype. A comparative study based on Mean Square Error is then performed between the proposed approach and other existing methods. As a result, the improved Grey MsPSO is well adopted to find an optimal model for the real processes with high accuracy and strong generalization ability.

An Orthogonal Multi-Swarm Cooperative PSO Algorithm with a Particle Trajectory Knowledge Base

A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for multi-dimensional optimization problems, but also conceives a new adaptive cooperation mechanism to accomplish the information interaction among swarms and particles. Experiments are conducted on a set of benchmark functions, and the results show its better performance compared with traditional PSO algorithm in aspects of convergence, computational efficiency and avoiding premature convergence.

Nonholonomic motion planning for minimizing base disturbances of space manipulators based on multi-swarm PSO

SUMMARYBecause space manipulators must satisfy the law of conservation of momentum, any motion of a manipulator within a space-manipulator system disturbs the position and attitude of its free-floating base. In this study, the authors have designed a multi-swarm particle swarm optimization (PSO) algorithm to address the motion planning problem and so minimize base disturbances for 6-DOF space manipulators. First, the equation of kinematics for space manipulators in the form of a generalized Jacobian matrix (GJM) is introduced. Second, sinusoidal and polynomial functions are used to parameterize joint motion, and a quaternion representation is used to represent the attitude of the base. Moreover, by transforming the planning problem into an optimization problem, the objective function is analyzed and the proposed algorithm explained in detail. Finally, numerical simulation results are used to verify the validity of the proposed algorithm.

Hybrid multi‐swarm particle swarm optimisation based multi‐objective reactive power dispatch

Most of the real-world optimisation problems are subject to different types of constraints and are known as constrained optimisation problems. Reactive power dispatch (RPD) in electrical power system is also a non-linear, multi-objective or a single objective constrained optimisation problem. In this study, hybrid multi-swarm particle swarm optimisation (HMPSO) algorithm has been proposed to solve the RPD problem. HMPSO is one of the recently proposed population based search algorithm, in which the existing swarm is partitioned into several sub-swarms. Particle swarm optimisation is applied as the search engine for each sub-swarm. In addition, to explore more promising regions of the search space, differential evolution (DE) algorithm is implemented to improve the personal best of each particle. The RPD problem is formulated as non-linear, constrained multi-objective optimisation problem with equality and inequality constraints for minimisation of power losses and improvement of voltage profile simultaneously. To find the Pareto optimal set for RPD problem, weighted sum method has been applied. Afterwards, for finding the preferred solution out of the Pareto-optimal set, fuzzy membership function has been used. Effectiveness of the HMPSO algorithm has been verified on the standard IEEE 30-bus and a practical 75-bus Indian systems.