Basic Particle Swarm Optimization Algorithm Research Articles

Particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks

With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. Artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

A new automatic forecasting method based on a new input significancy test of a single multiplicative neuron model artificial neural network

ABSTRACT The model adequacy and input significance tests have not been proposed as features for the specification of a single multiplicative neuron model artificial neural networks in the literature. Moreover, there is no systematic approach based on hypothesis tests for using single multiplicative neuron model artificial neural networks for forecasting purposes like classical time series forecasting methods. In this study, new methods are proposed to solve these problems. The performance of the proposed test procedures is investigated in a simulation study. According to simulation results, the proposed tests have very good performance. Moreover, the test procedures are illustrated by using two real-world examples. The second contribution of the paper is that an automatic forecasting method is proposed based on input significance and model adequacy tests and the particle swarm optimization-based learning algorithm. The proposed automatic forecasting method is applied to M4 competition hourly data sets, and it is the best pure machine learning method among others in the competition. The proposed automatic forecasting method is more accurate than all benchmarks, such as MLP, RNN, and ETS, which were proposed by the competition organizers.

Hybrid and enhanced PSO: Novel first order reliability method-based hybrid intelligent approaches

Computing the sensitivity vector in the traditional first order reliability method may provide inaccurate reliability outcomes for discrete performance functions and inefficient computation burden for high-dimensional problems. In this study, two improved particle swarm optimization algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis. The abilities for convergence speed and global convergence of the particle swarm optimization algorithm are improved using a novel hybrid method called particle swarm optimization-based harmony search algorithm (PSO–HS), and enhanced particle swarm optimization (EPSO). The proposed methods use a dynamic self-adaptive term to execute the local adjusting process. Using twelve numerical-based engineering problems, the structural reliability frameworks developed based on modified versions of particle swarm optimization algorithms are compared to numerous FORM algorithms and the current metaheuristic methods. Results indicated that the novel proposed methods using the improved PSO algorithms are more robust and efficient than the analytical FORM methods for solving high-dimensional engineering problems. Furthermore, compared to the previous metaheuristic approaches, the suggested methods enabled faster convergence.

Optimized Control Strategy for Photovoltaic Hydrogen Generation System with Particle Swarm Algorithm

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

Algorithm for wireless sensor networks in ginseng field in precision agriculture.

In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng.

Search in forest optimizer: a bioinspired metaheuristic algorithm for global optimization problems

The present research proposes a new particle swarm optimization-based metaheuristic algorithm entitled “search in forest optimizer (SIFO)” to solve the global optimization problems. The algorithm is designed based on the organized behavior of search teams looking for missing persons in a forest. According to SIFO optimizer, a number of teams each including several experts in the search field spread out across the forest and gradually move in the same direction by finding clues from the target until they find the missing person. This search structure was designed in a mathematical structure in the form of intragroup search operators and transferring the expert member to the top team. In addition, the efficiency of the algorithm was assessed by comparing the results to the standard representations and a problem with the genetic, grey wolf, salp swarm, and ant lion optimizers. According to the results, the proposed algorithm was efficient for solving many numerical representations, compared to the other algorithms.

Jiles-Atherton-Based Hysteresis Identification of Joint Resistant Torque in Active Spacesuit Using SA-PSO Algorithm

To eliminate the influence of spacesuits’ joint resistant torque on the operation of astronauts, an active spacesuit scheme based on the joint-assisted exoskeleton technology is proposed. Firstly, we develop a prototype of the upper limb exoskeleton robot and theoretically analyse the prototype to match astronauts’ motion behavior. Then, the Jiles-Atherton model is adopted to describe the hysteretic characteristic of joint resistant torque. Considering the parameter identification effects in the Jiles-Atherton model and the local optimum problem of the basic PSO (particle swarm optimization) algorithm, a SA- (simulated annealing-) PSO algorithm is proposed to identify the Jiles-Atherton model parameters. Compared with the modified PSO algorithm, the convergence rate of the designed SA-PSO algorithm is advanced by 6.25% and 20.29%, and the fitting accuracy is improved by 14.45% and 46.5% for upper limb joint model. Simulation results show that the identified J-A model can show good agreements with the measured experimental data and well predict the unknown joint resistance torque.

Chaotic Particle Swarm Optimization with Attractive Search Space Border Points for Optimal Reactive Power Dispatch

This paper presents a reliable approach for optimal reactive power dispatch based on chaotic particle swarm optimization with attractive search space border points, where the particles are randomly attracted to the boundary points of the search space in each direction avoiding stagnation of the population. The introduction of chaotic dynamics improves the stability and rate of convergence. The algorithm is further improved by using Latin Hypercube Sampling (LHS) to create diversity in the population. The proposed algorithm is used for optimal reactive power dispatch with three objective functions, namely: minimization of real power loss, voltage stability index, and sum squared voltage deviations. The algorithm is tested on a standard 30-bus system of the Institute of Electrical and Electronics Engineers (IEEE) and on a practical 75-bus Indian Power System. The results obtained with the proposed algorithm are compared with the conventional interior point method and the basic particle swarm optimization algorithm, and the effectiveness of the proposed algorithm is demonstrated.

Analysis of the Relationship between Dance Action and Health Psychology in the Process of Dance Performance Teaching Environment.

One of the public fundamental disciplines that is typically put up among the professional teaching units in universities is dance. In order for this sports project with fitness, mental health, and aesthetic functions to be widely developed in universities, the use of reasonable, scientific, and targeted teaching methods can effectively improve the instructional effect. At the same time, it has further promoted the quality of education in universities and implemented the guiding ideology of “health first.” In order to avoid the classifier's performance-degrading effects brought on by the high dimension, this research suggests combining the classifier's psychological stress identification algorithm with a particle swarm optimization (PSO) approach. The experimental findings reveal that the PSO-SVM algorithm, PSO-BP algorithm, improved PSO-SVM algorithm, and improved PSO-BP algorithm, respectively, have recognition rates for psychological stress of 82.50%, 84.50%, 90.17%, and 94.83%. Additionally, the recognition rates of the improved PSO classifier are significantly higher than those of the basic PSO algorithm, demonstrating the improved PSO algorithm's strong generalization ability in optimization.

Profit-Aware Edge Server Placement

In a 5G network, mobile-edge computing (MEC) plays a key role in providing low access delay services. The placement of edge servers not only determines the quality of services on the user side but also affects the profit of running a MEC system. In this article, we study how to properly place edge servers so as to guarantee the access delay and maximize the profit of edge providers. We first propose a profit model which involves both access delay and energy consumption. In this model, we take the 5G user plane function (UPF) into consideration to calculate access delay for the first time. Then, we devise a particle swarm optimization-based algorithm to optimize the profit. In the algorithm, we introduce a weight value <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> to guarantee the access delay and assign base stations properly. Moreover, a service-level agreement is adopted to balance the tradeoff between access delay and energy consumption. We take advantage of our 5G network emulator called mini5Gedge and data set from Shanghai Telecom to conduct massive experiments. The results show that our algorithm stands out in terms of achieving the highest profit.

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.

PSO-based Power-Driven X-Routing Algorithm in semiconductor design for predictive intelligence of IoT applications

As the Internet of Things (IoT) becomes more and more intelligent, a new computing paradigm, predictive intelligence is incorporated into many IoT applications. The devices of predictive intelligence in IoT applications must consider the power and delay consumption. As the Power-Driven X-Routing (PDXR) problem model under the advanced semiconductor design, the length-restricted condition in the multi-dynamic voltage model is introduced to save power consumption and the X-architecture is introduced to better reduce the wirelength to optimize the chip delay. To this end, an effective particle swarm optimization-based power-driven length-restricted X-routing algorithm is proposed for predictive intelligence in IoT applications. Firstly, a pre-calculated lookup table is designed to provide fast information query for the subsequent algorithm flow. Secondly, an improved particle swarm optimization algorithm is presented for the discrete PDXR problem. Thirdly, in the adjustment phase, the choice of intermediate nodes is expanded, and is no longer limited to the corner points of obstacles. Fourthly, a removal strategy of redundant points is proposed to optimize the routing path. Finally, the wirelength is further reduced by a local topology optimization strategy. The experimental results show that the proposed algorithm can achieve the best wirelength cost at a very fast speed under the constraint of restricted wirelength, so as to better satisfy the demand of the power and delay performance of semiconductor design for predictive intelligence in IoT applications.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

Collaborative Beamforming (CBF) is an essential tool towards increasing transmission range in Wireless Sensor Networks (WSNs). Owing to the random and complex nature of WSNs, development and use of improved metaheuristic algorithms in CBF is of essence. Particle Swarm Optimization (PSO) algorithm is a good candidate for use in CBF owing to its simplicity and low computation complexity. However, the basic PSO algorithm suffers from premature convergence particularly in highly multimodal functions (typical of CBF). This paper delves into the development and application of an improved Particle Swarm Optimization (PSO) algorithm in CBF. A new fuzzy-logic based confidence and inertia weight parameters adaptation scheme has been developed with an aim of enhancing exploration and exploitation capabilities of the PSO algorithm. Normalized particle quality and iteration count have been used as the inputs to the designed fuzzy-logic inference system. The fuzzy logic based parameters adaptation scheme has been implemented in the form of a lookup table to minimize “on-line” computation complexity. Furthermore, a particle culling/ re-initialization procedure is utilized at half the number of maximum iterations to enhance overall swarm diversity. The modified PSO algorithm has been christened Culled Fuzzy Adaptive Particle Swarm Optimization (CFAPSO) algorithm. The developed CFAPSO algorithm is noted to outperform other metaheuristic algorithms in a statistical performance analysis procedure (on the basis of a set of standard unimodal and multimodal functions). Upon application to CBF, the CFAPSO algorithm is found to generate a beamsteering outcome statistically identical to that of conventional beamsteering.

An Improved Particle Swarm Optimization for Prediction of Accident Severity

In recent time, road traffic accidents are increased day by day due to incremental growth in number of vehicles. Traffic accidents are also the crucial and important issue for achieving the sustainable transportation development. One of the tasks of sustainable transportation is to reduce the number of accidents and also design the traffic assessment policy. Many researchers consider the traffic accident issue of sustainable transportation and developed prediction models for measuring severity of accidents. But the accuracy of accident severity is one of the major issues. In this work, an attempt is made to improve the accuracy of accident severity. To achieve the same, a particle swarm optimization-based algorithm is applied for evaluating the accident severity. Prior to implement the PSO, two modification are incorporated into PSO algorithm, called improved PSO. These modifications can be described as mutation operator and trail candidate generation. The performance of improved PSO is examined over accident traffic severity dataset and results are evaluated using accuracy, recall@5 and precision@5 metrics. Several existing techniques are considered for comparing the results of IPSO algorithm. It is revealed that IPSO achieves more accurate results among all techniques.

Secure and Optimal Secret Sharing Scheme for Color Images

Due to contemporary communication trends, the amount of multimedia data created and transferred in 5G networks has reached record levels. Multimedia applications communicate an enormous quantity of images containing private data that tend to be attacked by cyber-criminals and later used for illegal reasons. Security must consider and adopt the new and unique features of 5G/6G platforms. Cryptographic procedures, especially secret sharing (SS), with some extraordinary qualities and capacities, can be conceived to handle confidential data. This paper has developed a secured (k, k) multiple secret sharing (SKMSS) scheme with Hybrid Optimal SIMON ciphers. The proposed SKMSS method constructs a set of noised components generated securely based on performing hash and block ciphers over the secret image itself. The shares are created and safely sent after encrypting them through the Hybrid Optimal SIMON ciphers based on the noised images. This is a lightweight cryptography method and helps reduce computation complexity. The hybrid Particle Swarm Optimization-based Cuckoo Search Optimization Algorithm generates the keys based on the analysis of the peak signal to noise ratio value of the recovered secret images. In this way, the quality of the secret image is also preserved even after performing more computations upon securing the images.

Recovery trajectory planning for the reusable launch vehicle

Trajectory planning method for the entire recovery process of the reusable launch vehicle is studied. Firstly, a revised trajectory correction method (RTCM) is proposed to reduce the maximum normal aerodynamic load in the endo-atmosphere unpowered descent phase and improve the vehicle's landing accuracy on the influence of wind field. It consists of two parts, namely moving the ignition point of the vertical landing phase on the horizontal plane and introducing preset steady wind field into trajectory planning. Secondly, a compound trajectory planning method, which combines Particle Swarm Optimization algorithm (PSO) with Polynomial Guidance Law (PGL), is proposed to complete the optimization task of the entire recovery process. Employing PSO algorithm can avoid the complex calculation of additional angle of attack caused by the preset steady wind field in traditional trajectory correction. To overcome the premature convergence of basic PSO, the traditional re-initialization mechanism is improved. Finally, the guidance simulation of two phases greatly affected by wind is accomplished. The effectiveness of the proposed methods is demonstrated with some scenarios and Monte Carlo simulation.

Comparative Analysis of Solution Methods for Swing Equation in Power System for Transient Stability Studies

In power system swing equation plays a remarkable role in investigating the transient stability. Disturbances in system creates imbalance and it may lead to lose the synchronism. Solution of swing equation will help the operators to analyze the stability of the system during the disturbances and initiate the protective mechanism to save the system. The non linear differential swing equation is complex in nature. Commonly used numerical methods to solve swing equations like Point by Point, Modified Euler’s and Runge Kutta have explained in the paper. Along with numerical methods, two other methods, evolutionary algorithm based Particle Swarm Optimization method and analytical based Maximum Lyapunov Exponent method has also been investigated in this paper.

Study on Active Disturbance Rejection Control of a Bearingless Induction Motor Based on an Improved Particle Swarm Optimization–Genetic Algorithm

To overcome the limitations that active disturbance rejection control (ADRC) system of a bearingless induction motor (BIM) has difficulty in tuning parameters depending on experience to select parameters, an ADRC strategy based on improved particle swarm optimization-genetic algorithm (IPSO-GA) is proposed. Based on the orientation of the air-gap magnetic field, the first-order and the second-order ADRC are, respectively, designed for the BIM rotation and suspension parts according to the different order of the BIM system. Then, the parameters of the basic particle swarm optimization algorithm are optimized by considering the characteristics of the basic particle swarm algorithm parameters, and the crossover and mutation operations are introduced to enhance global search capability. Meanwhile, through the performance test based on the test function, the performance of IPSO-GA is verified, and the parameters of ADRC are adjusted by IPSO-GA. In addition, this strategy is analyzed with simulation in MATLAB/Simulink and verified on an experimental prototype. Both simulation and experimental results show that the proposed strategy not only effectively improves the starting performance and antidisturbance ability of the BIM but also reduces the maximum radial offset of the rotor and improves the suspension precision of the motor.

Predicting Academic Performance of Deaf Students Using Feed Forward Neural Network and An Improved PSO Algorithm

Literacy rate of deaf students is very less in India. So there is a need to build an effective academic prediction model for identifying weak deaf students. Many machine learning techniques like Decision tree, Support Vector Machine, Neural Network are used to build prediction models. But the most preferred technique is neural network. It is found out that regression model build with neural networks takes more time to converge and the error rate is quite high. To solve the problems of neural network, we use Particle Swarm Optimization (PSO) for weight adjustment in the neural network. But, one of the main drawback of PSO lies in setting the initial parameters. So, a new PSO algorithm which determines the initial weight of the neural network using regression equation is proposed. The results show that neural network build with the proposed PSO algorithm performs well than neural network build with basic PSO algorithm. The Mean Square Error (MSE) achieved in this work is 0.0998, which is comparatively less than many existing models.

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

In this paper, we seek to provide unmanned ground vehicles with positioning service using ultrawideband (UWB) technology, a high-accuracy positioning approach. UWB is chosen for two distinct reasons. First, it does not rely on global navigation satellite systems like GPS, making it able to be applied indoors or in an environment where GPS signal is unstable. Second, it is immune to interference from other signals and accurate enough to guide unmanned ground vehicles moving precisely in a complex environment within a narrow road. In this paper, three UWB base stations are aggregated as a group in a 2D space for localization. A large number of tests are performed with a UWB base station cluster in order to validate its positioning performance. Based on the experiment results, we further develop a dynamic particle swarm optimization-based algorithm and a genetic algorithm to deploy multiple clusters of UWB base stations to cover an area of interest. The performance of the proposed algorithms has been tested through a series of simulations. Finally, experiments using unmanned ground vehicles are carried out to validate the localization performance. The results confirm that the robots can follow complex paths accurately with the proposed UWB-based positioning system.