Standard Particle Swarm Algorithm Research Articles

Research on photovoltaic dynamic MPPT algorithm based on adaptive PSO optimization

This paper proposes a photovoltaic (PV) dynamic maximum power point tracking algorithm based on improved PSO (particle swarm optimization) optimization in response to the problems associated with low tracking accuracy, poor immunity, and the ease of falling into local optimization, as well as the failure of the traditional MPPT algorithm (maximum power point tracking algorithm) under partial shading conditions. Firstly, three traditional MPPT algorithms are compared and analyzed, followed by simulation testing under standard and partial shading conditions. The advantages and disadvantages of three traditional algorithms are analyzed. Secondly, it is proposed that dynamic inertia weights and learning factors be applied synchronously during the optimization process in order to speed up the tracking speed of particle swarm optimization. In order to evaluate the effectiveness of different algorithms, it is best to simulate them under static and dynamic conditions. In comparison to the standard particle swarm algorithm and three other traditional algorithms, the proposed algorithm is capable of tracking the maximum power point quickly and accurately under conditions of uniform illumination and static and dynamic partial shading. There is a faster convergence speed as well as a greater degree of accuracy at steady state.

A model for the application of big data analytics in cost prediction and control in construction projects

Abstract Construction project cost control is an important aspect of project management. This paper aims to provide an effective cost control management mode for the construction industry by studying the application of big data technology in cost prediction and control management of construction projects. The standard particle swarm algorithm is optimized by establishing a particle object element model, performing iterative and hierarchical updating of particles, and introducing the Tent mapping strategy. Then it is used in the optimization of the Extreme Learning Machine algorithm to design a construction project cost prediction and control model based on APSO-LLM. The index system of the construction project cost prediction and control model is constructed, and the convergence effect, prediction effect, and case application of the model are analyzed. The APSO-LLM model enhances the convergence speed and search accuracy, boosts the optimal fitness by 0.037, and maintains a construction cost prediction error below 3%. The study proves the APSO-LLM model’s applicability, accuracy, and good prediction effect in cost prediction and construction project control.

Traveling Salesman Problem Solving Based on Hybrid Particle Swarm Optimization Algorithm

Genetic algorithms all the methods used to solve the Traveling Agent (TSP) problem, the use of genetic algorithms on TSP problems is more common than other methods, and it is also possible to achieve global search and obtain the best values. Compared with the traditional genetic algorithm, the particle swarm algorithm is not only easy to be applied, but also can be widely used because of the function of rapid search, but at the same time, the shortcomings of the traditional standard particle swarm algorithm are more prominent, and it is easy to fall into the situation of optimal solution of local problems. So first of all, we use the classical TSP algorithm to propose a mixed particle swarm algorithm, which solves the TSP problem by using the selection, crossover, variation and other operators of the traditional genetic algorithm, and through the conclusion of the solution, it can be shown that the method not only greatly improves the search function of the traditional standard particle swarm, but also greatly improves the convergence speed while obtaining the approximate optimal solution.

Day-Ahead Operation Analysis of Wind and Solar Power Generation Coupled with Hydrogen Energy Storage System Based on Adaptive Simulated Annealing Particle Swarm Algorithm

As the low-carbon economy continues to evolve, the energy structure adjustment of using renewable energies to replace fossil fuel energies has become an inevitable trend. To increase the ratio of renewable energies in the electric power system and improve the economic efficiency of power generation systems based on renewables with hydrogen production, in this paper, an operation optimization model of a wind–solar hybrid hydrogen energy storage system is established based on electrochemical energy storage and hydrogen energy storage technology. The adaptive simulated annealing particle swarm algorithm is used to obtain the solution, and the results are compared with the standard particle swarm algorithm. The results show that the day-ahead operation scheme solved by the improved algorithm can save about 28% of the system operating cost throughout the day. The analytical results of the calculation example revealed that the established model had fully considered the actual operational features of devices in the system and could reduce the waste of wind and solar energy by adjusting the electricity purchased from the power grid and the charge and discharge powers of the storage batteries under the mechanism of time-of-use electricity price. The optimization of the day-ahead scheduling of the system achieved the minimization of daily system operation costs while ensuring that the hydrogen-producing power could meet the hydrogen demand.

An Algorithm for Optimal Allocation of Water Resources in Receiving Areas Based on Adaptive Decreasing Inertia Weights

As the biggest rigid constraint for high-quality economic and social development, how to improve the carrying capacity of water resources, realize the stable and coordinated development of water resources-ecological environment-economic and social integrated system, and provide water resources guarantee for regional transformation and upgrading development is a major issue in the current social development. This paper firstly selects the minimum loss of water resources allocation as the objective function for mathematical modelling, chooses the particle swarm algorithm as the objective algorithm, and proposes a particle swarm algorithm based on the standard particle swarm algorithm with improved adaptive decreasing inertia weights. It is a time-varying process for the inertia weights and acceleration factors of the standard particle swarm algorithm so that they change nonlinearly with the continuous advancement of the iterative optimization seeking process, thus improving the convergence accuracy and speed of the algorithm and reducing the risk of falling into local optimum solutions at a later stage. Finally, based on the actual installation of the current water distribution reactive power compensation device, the shunt distributor set is selected as the reactive power compensation device, sensitivity analysis is applied to the load nodes for sensitivity calculation, and the nodes requiring compensation are connected to the shunt distributor set for flexible and optimal configuration. The stronger local search capability of the inertia weight adaptive decreasing algorithm is utilized in the generation process of new particles to perform a local search operation for particles, which avoids premature convergence and improves the search performance of the algorithm. To realize the rational allocation of water resources, a multiobjective receiving area water resources optimization allocation model with maximum water supply benefit, minimum regional water shortage, and minimum pollutant emission is established.

Improved Heat Exchanger Network Synthesis without Stream Splits Based on Comprehensive Learning Particle Swarm Optimizer.

In this paper, an improved heat exchanger network (HEN) synthesis method based on the comprehensive learning particle swarm optimizer algorithm (CLPSO) is proposed to synthesize HENs without stream splits. Compared with the standard particle swarm algorithm, CLPSO employs a novel learning strategy that preserves the diversity of the swarm to discourage premature convergence. However, while the algorithm’s global exploration capability is enhanced, the local search capability decreases and the convergence speed becomes slow. In addition, the solution quality of CLPSO is largely determined by the randomly generated particles’ best previous position (pbest) during initialization. Hence, the solution may be unstable due to different pbest. For the abovementioned considerations, this paper proposes a new HEN initialization and renovation method to improve the quality of pbest, reduce the initial cost, and retain the obtained optimization results as much as possible in the optimization process to speed up the convergence of the algorithm. Four typical cases are simulated to verify the effectiveness of the proposed method. This method only needs a single-level optimization algorithm to obtain high-quality solutions, which will give it a bright prospect in research and application.

Research on MPPT control strategy of photovoltaic cells under multi-peak

Under the condition of partial shadow occlusion, the P-U curve of the photovoltaic array exhibits the characteristics of multi-peak output. The traditional maximum power point tracking (MPPT) algorithm is easy to fail, and the particle swarm algorithm, as multi-objective optimization algorithm, is characterized by its low volatility and fast convergence speed. It is applied to the optimization process of multi-peak photovoltaic cells. Aiming at the multi-peak output of the photovoltaic array, this paper builds a simulation model based on the standard particle swarm algorithm and the perturbation and observation method through MATLAB/Simulink. The simulation results show that compared with the traditional perturbation and observation method, the standard particle swarm algorithm can reduce fluctuations when the power is stable and can quickly track the output multi-peak maximum power point when the light intensity changes suddenly.

GB decomposition method based on standard particle swarm algorithm

Groom-Bailey decomposition method is the most widely used method of tensor decomposition of magnetotelluric impedance, which is referred to as GB decomposition. The essence of the GB decomposition method is to solve the nonlinear over-determined equations derived from the GB decomposition definition. Traditional GB decomposition methods often use linear optimization methods to solve the equations, which have low calculation accuracy and slow speed. In this regard, this article introduces the standard particle swarm optimization algorithm into the GB decomposition method for the first time. The standard particle swarm algorithm is used to solve the equations. The global optimization method is used to implement the magnetotelluric GB decomposition method, and the synthesized single-point single-frequency data is used. The 3D / 2D model forward data is used to perform decomposition experiments. The experimental results show that the standard electromagnetic particle decomposition method based on the standard particle swarm algorithm is effective.

An Improved Particle Swarm Algorithm with Heuristic Factors and Empirical Operators

An improved particle swarm algorithm (PSA) is proposed based on the universal framework of the intelligence optimization algorithms (IOA). Regarded as increment to the standard PSA, the improved PSA takes the essence of the standard PSA, and additionally utilizes various heuristic factors and different empirical operators introduced by multiple other IOA. The capability of global and local optimization can be strengthened by the free search, the differential evolution and the quantum rotation comprehensively. The genetic variation is adopted to avoid premature convergence, while the greed strategy is adopted to guarantee monotonous convergence. The numerical simulation indicates that the improved PSA achieves better performance than the standard PSA.

Fatigue life prediction for vibration isolation rubber based on parameter‐optimized support vector machine model

AbstractGiven the small sample size, nonlinearity, and large dispersion of the measured data of fatigue performance for vibration isolation rubbers, the fatigue life prediction model for vibration isolation rubber materials was established using a support vector machine (SVM). A modified gravity search algorithm (MGSA) is proposed to optimize the parameters of the SVM. Using environmental temperature, the Rockwell hardness of the rubber compound, and the engineering strain peak as the input variables, the model was trained based on the experimental fatigue data of vibration isolation rubber materials. For comparison, the standard genetic algorithm, the standard particle swarm algorithm, and the standard simulated annealing algorithm are also implemented. Moreover, a back propagation neural network regression model is applied to the life prediction, with the conclusion that the prediction accuracy and the efficiency of MGSA are better than those of extant methods. This work can provide reference for further fatigue life prediction and structural improvement of rubber parts.

Asymmetric delay feedback stochastic resonance detection method based on prior knowledge particle swarm optimization

For the adjustable parameters stochastic resonance system, the selection of the structural parameters plays a decisive role in the performance of the detection method. The vibration signal of rotating machinery is non-linear and unstable, and its weak fault characteristics are easily concealed by noise. Under strong background noise interference, the detection of fault features is particularly challenging. Therefore, a type of weak fault feature extraction method, named knowledge-based particle swarm optimization algorithm for asymptotic delayed feedback stochastic resonance (abbreviated as KPSO-ADFSR) is proposed. Through deduction under adiabatic approximation, we observe that both the asymmetric parameters, the length of delay and the feedback strength, impact the potential function. After adjusting the asymmetric parameters of the system, the output signal-to-noise ratio (SNR) is used as the fitness function, and the setting of the relationship between the noise intensity and barrier height is used as the prior knowledge of the particle swarm algorithm. Through this algorithm, the delay length and the feedback strength are optimized. This method achieves global optimization of system parameters in a short time; it overcomes the shortcomings of the traditional stochastic resonance method, which has a long convergence time and tends to easily fall into local optimization. It can effectively improve the detection of weak fault features. In the bearing rolling body pitting corrosion failure experiment and steel field engineering experiment, the proposed method could extract the characteristics of a weak fault more effectively than the traditional stochastic resonance method based on the standard particle swarm algorithm.

Optimization of a Low-Voltage Load Switch for a Smart Meter Based on a Double Response Surface Model

The existing electric energy meter load switches have issues such as a low electrical life, poor resistance to short-circuit currents, high contact point resistivity and severe heating. Research has thus been conducted on the optimization of the electric energy meter built-in load switch mechanism to establish a double response surface model for the electromagnetic attractive force and reed reaction force. The model introduces a niche fitness sorting strategy and a Gaussian mutation mechanism that are based on the standard particle swarm algorithm and form an improved multi-objective particle swarm optimization algorithm in which the static attractive force is regarded as the optimization objective. The multi-objective optimization of the structural parameters of the electromagnetic system and the reed system was carried out, and the optimization results were applied in the devices’ design and implementation. The new structure improves the electromagnetic suction and reed force at the suction position. And the results from simulation show that the proposed method exceeds that of the back propagation neural network method in achieving the optimization goal and the new structure can effectively improve the performance of meter built-in load switch by avoiding the recoil phenomenon.

A hybrid PSO-GA algorithm for optimization of laminated composites

This paper presents a new hybrid Particle Swarm Algorithm (PSO) for optimization of laminated composite structures. The method combines the standard PSO heuristics with Genetic Algorithm operators in order to improve the algorithm performance. Thus, operations that are important to the optimization of laminated composites such as mutation and layer swap are incorporated into the method. A specially designed encoding scheme is used to represent the laminate variables and the associated velocities. A study is carried-out to select the best variant of the proposed method for the optimization of laminated composites, considering different swarm topologies and genetic operators. Both strength maximization and weight minimization problems are considered. A meta-optimization procedure is used to tune the parameters of each variant in order to avoid biased results. The results showed that the proposed method led to excellent results for both traditional and dispersed laminates, representing a significant improvement over the standard PSO algorithm.

Combining K-Means and K-Harmonic with Fish School Search Algorithm for data clustering task on graphics processing units

Data clustering is related to the split of a set of objects into smaller groups with common features. Several optimization techniques have been proposed to increase the performance of clustering algorithms. Swarm Intelligence (SI) algorithms are concerned with optimization problems and they have been successfully applied to different domains. In this work, a Swarm Clustering Algorithm (SCA) is proposed based on the standard K-Means and on K-Harmonic Means (KHM) clustering algorithms, which are used as fitness functions for a SI algorithm: Fish School Search (FSS). The motivation is to exploit the search capability of SI algorithms and to avoid the major limitation of falling into locally optimal values of the K-Means algorithm. Because of the inherent parallel nature of the SI algorithms, since the fitness function can be evaluated for each individual in an isolated manner, we have developed the parallel implementation on GPU of the SCAs, comparing the performances with their serial implementation. The interest behind proposing SCA is to verify the ability of FSS algorithm to deal with the clustering task and to study the difference of performance of FSS-SCA implemented on CPU and on GPU. Experiments with 13 benchmark datasets have shown similar or slightly better quality of the results compared to standard K-Means algorithm and Particle Swarm Algorithm (PSO) algorithm. There results of using FSS for clustering are promising.

Study of preferential vector of particle swarm with hierarchical reinforcement

A preferential vector algorithm of particle swarm with hierarchical reinforcement learning is proposed to solve the balance problem of global searching range and local searching precision, and the problem of fixedly adjusting strategy of inertia weight. Firstly, the preferential particle position with crossover operation was introduced on the standard particle swarm algorithm. The sequential adding operations on single step of particle position were divided into the seeking of middle particles. The operations of crossover and mutation were combined to keep the elite particle swarm. Secondly, the adjusting strategies of inertia weight were treated as the actions. The hierarchical learning was executed in every iteration of particle swarm and the strategy with maximal discounted profit was selected. The experiment proved the validity of the algorithm.

Integration of Entropy Maximization and Quantum Behaved Particle Swarm Algorithm for Unsupervised Change Detection of MR Skull Bone Lesions

Entropy is the measure of randomness in a system whereas the entropy maximization procedure leads to the most probable state of a system behaviour.Entropy maximization using an optimization algorithm is used to find the threshold of the MR image of the brain. Standard Particle Swarm algorithm sufferes from stagnation. An automatic regrouping mechanism is used to deal with the stagnation. An Quantum Particle Swarm algorithm together with Entropy maximization helps us to get the most probable threshold value which correctly segments the lesions from the background in MR of brain. Using change detection algorithm the segmented object of the MR at time tx is compared with another object of the MR at the time ty .The proposed method is applied on variety of MR images having lesions and gives favourable results in identifying changes taking place in the human brain. General Terms Change detection, Magnetic Resonance Imaging, Hybrid Particle Swarm Algorithm

Underwater Wireless Sensor Network Deployment Based on Chaotic Particle Swarm Optimization Algorithm

The application of particle swarm optimization algorithm in underwater wireless sensor node deployment strategy was studied. The chaotic particle swarm optimization algorithm was proposed. Set up a function variance to determine whether the particles enter the premature state. Then chaos searched and took the chaotic search optimal solution vector as the global optimization solution to guide the particles quickly jump out of local optimum, accelerate the convergence speed and to overcome the premature problem of standard particle swarm algorithm brought. The network coverage was the fitness function to optimize the deployment of nodes question. The chaotic particle swarm optimization algorithm was used to wireless sensor network node deployment strategy. The experimental results showed that this method could effectively improve the convergence speed and increase network coverage.

Multiswarm Particle Swarm Optimization with Transfer of the Best Particle

We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems.

Particle Swarm Optimization Algorithm with Chaotic Mapping Model

Particle swarm optimization algorithm is easy to reach premature convergence in the solution process, and fall into the local optimal solution. Aiming at the problem, this paper proposes a particle swarm optimization algorithm with chaotic mapping (CM-PSO). The algorithms uses chaotic mapping function to optimize the initial state of population, im- prove the probability of obtain optimal solution. Then, CM-PSO algorithm introduces nonlinear decreasing strategy on the inertia weight to avoid local optimal solution. In the experimental stage, four different functions are used to validate the performance of the algorithm. The experimental results show that, compared with the standard particle swarm algorithm, CM-PSO algorithm has strong global searching ability, can effectively avoid the premature convergence problem, and en- hance the ability of the algorithm to escape from local optima. Although the algorithm consumes time is slightly in- creased, it is worth for getting the global optimal solution with such cost.

Image Register Based on PSO and Error Mechanism

When applying the standard particle swarm algorithm to image registering, it has a problem of convergence and the speed of the convergence. Aiming at this, we proposed a modified particle swarm optimization; a simple error correction mechanism is introduced into the update process of the particle, so that the particle’s error evolution can be corrected in time. To test the novel algorithm, we used a typical function to identify it. It can be seen from the simulation results that the convergence ability and the convergence speed of the new algorithm was increased effectively. Then applying the PSO algorithm to image registering, the experiment result is good.