scholarly journals Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

2016 ◽  
Author(s):  
Qin Zheng ◽  
Zubin Yang ◽  
Jianxin Sha ◽  
Jun Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Extreme Values ◽  
Initial Perturbation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Prediction Time ◽  
The Impact

Abstract. In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies the certain constraint condition and causes the largest prediction error at the prediction time. The CNOP method has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that: (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model on the prediction variable will lead to failure of the ADJ-CNOP method; (2) when the objective function has multiple extreme values, ADJ-CNOP has large probability to produce local CNOPs, hence make false estimation on the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithms, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. Besides, to check the estimation accuracy of the LBMPT presented by the PSO-CNOP and the ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes, and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

scholarly journals Conditional nonlinear optimal perturbations based on the particle swarm optimization and their applications to the predictability problems

2017 ◽  
Vol 24 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Qin Zheng ◽  
Zubin Yang ◽  
Jianxin Sha ◽  
Jun Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Extreme Values ◽  
Initial Perturbation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Strong Nonlinearity ◽  
Swarm Optimization ◽  
Prediction Time ◽  
The Impact

Abstract. In predictability problem research, the conditional nonlinear optimal perturbation (CNOP) describes the initial perturbation that satisfies a certain constraint condition and causes the largest prediction error at the prediction time. The CNOP has been successfully applied in estimation of the lower bound of maximum predictable time (LBMPT). Generally, CNOPs are calculated by a gradient descent algorithm based on the adjoint model, which is called ADJ-CNOP. This study, through the two-dimensional Ikeda model, investigates the impacts of the nonlinearity on ADJ-CNOP and the corresponding precision problems when using ADJ-CNOP to estimate the LBMPT. Our conclusions are that (1) when the initial perturbation is large or the prediction time is long, the strong nonlinearity of the dynamical model in the prediction variable will lead to failure of the ADJ-CNOP method, and (2) when the objective function has multiple extreme values, ADJ-CNOP has a large probability of producing local CNOPs, hence making a false estimation of the LBMPT. Furthermore, the particle swarm optimization (PSO) algorithm, one kind of intelligent algorithm, is introduced to solve this problem. The method using PSO to compute CNOP is called PSO-CNOP. The results of numerical experiments show that even with a large initial perturbation and long prediction time, or when the objective function has multiple extreme values, PSO-CNOP can always obtain the global CNOP. Since the PSO algorithm is a heuristic search algorithm based on the population, it can overcome the impact of nonlinearity and the disturbance from multiple extremes of the objective function. In addition, to check the estimation accuracy of the LBMPT presented by PSO-CNOP and ADJ-CNOP, we partition the constraint domain of initial perturbations into sufficiently fine grid meshes and take the LBMPT obtained by the filtering method as a benchmark. The result shows that the estimation presented by PSO-CNOP is closer to the true value than the one by ADJ-CNOP with the forecast time increasing.

Neural Network Assisted Particle Swarm Optimization of Machining Process

2013 ◽  
Vol 581 ◽  
pp. 511-516
Author(s):  
Uros Zuperl ◽  
Franci Cus
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Machining Process ◽  
Neural Network Modeling ◽  
Swarm Optimization ◽  
Turning Operation ◽  
Optimization System

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

scholarly journals Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Prabha Umapathy ◽  
C. Venkataseshaiah ◽  
M. Senthil Arumugam
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Optimal Power ◽  
The Impact

This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization (PSO). The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight (CIW), a time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.

scholarly journals On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

2006 ◽  
Vol 2006 ◽  
pp. 1-17 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Single Stage ◽  
Hybrid Manufacturing ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Real Coded Genetic Algorithm ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

Particle Swarm Optimization Using Various Inertia Factor Variants

2011 ◽  
Vol 460-461 ◽  
pp. 54-59
Author(s):  
Jun Tang
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Time Varying ◽  
Swarm Optimization ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of manufacturing systems. Three different inertia factor, a constant inertia factor (CIF), time-varying inertia factor (TVIF), and global-local best inertia factor (GLbestIF), are considered with the particle swarm optimization(PSO) algorithm to analyze the impact of inertia factor on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia factor separately to compute the optimal control of the manufacturing system, and it is observed that the PSO with the proposed inertia factor yields better resultin terms of both optimal solution and faster convergence. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

scholarly journals The effect of acceleration coefficients in Particle Swarm Optimization algorithm with application to wind farm layout design

2020 ◽  
Vol 48 (4) ◽  
pp. 922-930
Author(s):  
Shafiqur Rehman ◽  
Salman Khan ◽  
Luai Alhems
Keyword(s):  
Particle Swarm Optimization ◽  
Design Problem ◽  
Wind Farm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Layout Design ◽  
Energy Output ◽  
Swarm Optimization ◽  
Wind Farm Layout ◽  
The Impact

Wind energy has become a strong alternative to traditional sources of energy. One important decision for an efficient wind farm is the optimal layout design. This layout governs the placement of turbines in a wind farm. The inherent complexity involved in this process results in the wind farm layout design problem to be a complex optimization problem. Particle Swarm Optimization (PSO) algorithm has been effectively used in many studies to solve the wind farm layout design problem. However, the impact of an important set of PSO parameters, namely, the acceleration coefficients, has not received due attention. Considering the importance of these parameters, this paper presents a preliminary analysis of PSO acceleration coefficients using the conventional and a modified variant of PSO when applied to wind farm layout design. Empirical results show that the acceleration coefficients do have an impact on the quality of final layout, resulting in better overall energy output.

Decision quality of the research project evaluation mechanism by using particle swarm optimization

2017 ◽  
Vol 55 (4) ◽  
pp. 745-765 ◽  
Author(s):  
Young Wook Seo ◽  
Kun Chang Lee ◽  
Sangjae Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Analysis ◽  
Research Performance ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Decision Quality ◽  
Research Fund ◽  
Swarm Optimization ◽  
Content Type ◽  
The Impact

Purpose For those who plan research funds and assess the research performance from the funds, it is necessary to overcome the limitations of the conventional classification of evaluated papers published by the research funds. Besides, they need to promote the objective, fair clustering of papers, and analysis of research performance. Therefore, the purpose of this paper is to find the optimum clustering algorithm using the MATLAB tools by comparing the performances of and the hybrid particle swarm optimization algorithms using the particle swarm optimization (PSO) algorithm and the conventional K-means clustering method. Design/methodology/approach The clustering analysis experiment for each of the three fields of study – health and medicine, physics, and chemistry – used the following three algorithms: “K-means+Simulated annealing (SA)+Adjustment of parameters+PSO” (KASA-PSO clustering), “K-means+SA+PSO” clustering, “K-means+PSO” clustering. Findings The clustering analyses of all the three fields showed that KASA-PSO is the best method for the minimization of fitness value. Furthermore, this study administered the surveys intended for the “performance measurement of decision-making process” with 13 members of the research fund organization to compare the group clustering by the clustering analysis method of KASA-PSO algorithm and the group clustering by research funds. The results statistically demonstrated that the group clustering by the clustering analysis method of KASA-PSO algorithm was better than the group clustering by research funds. Practical implications This study examined the impact of bibliometric indicators on research impact of papers. The results showed that research period, the number of authors, and the number of participating researchers had positive effects on the impact factor (IF) of the papers; the IF that indicates the qualitative level of papers had a positive effect on the primary times cited; and the primary times cited had a positive effect on the secondary times cited. Furthermore, this study clearly showed the decision quality perceived by those who are working for the research fund organization. Originality/value There are still too few studies that assess the research project evaluation mechanisms and its effectiveness perceived by the research fund managers. To fill the research void like this, this study aims to propose PSO and successfully proves validity of the proposed approach.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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