Combining inertia and constriction technique in the PSO applied to fault identification in a hydraulic system

2016 ◽  
Vol 231 (14) ◽  
pp. 2730-2740
Author(s):  
Jay Prakash Tripathi ◽  
Sanjoy Ghoshal
Keyword(s):  
Control Valve ◽  
Pso Algorithm ◽  
Fault Isolation ◽  
Mode Switching ◽  
Fault Identification ◽  
Accurate Identification ◽  
Inertia Weight ◽  
Directional Control ◽  
Parametric Fault ◽  
Constriction Coefficient

A novel methodology for simultaneous parametric fault isolation and mode switching identification in the spool motion of a Directional Control Valve (DCV), under multi fault assumption, has been reported in this paper. The shape of the profile traversed by the DCV spool was assumed to be trapezoidal in both healthy and faulty condition, but the slope of trapezoidal may change due to fault. Under this assumption, the real valued fault parameter and binary mode switching were identified by real valued Particle Swarm Optimization (PSO) alone instead of a combined real and binary valued PSO (Hybrid PSO). A novel PSO algorithm by combining the concepts of varying inertia weight (both increasing and decreasing trend) and constriction factor has been proposed in the article to achieve more accurate identification. Its validity was examined using an existing heuristic formula and by conducting several tests on a benchmark function used for fault identification. Superior improvement was observed in the identification with increasing inertia weight than that of widely used decreasing inertia weight, when combined with the constriction coefficient. A high pressure hydraulic circuit used in dumper and several other lifting machines was used as a simulation example.

2-Staged Reconstruction Optimization for High Voltage Distribution Network Based PSO Algorithm

2012 ◽  
Vol 516-517 ◽  
pp. 1503-1507
Author(s):  
Gui Hua Yuan ◽  
Lin Li ◽  
Jun Li ◽  
Zhi Jian Hu ◽  
Xiao Bo Li ◽  
...  
Keyword(s):  
High Voltage ◽  
Optimization Model ◽  
Distribution Network ◽  
Pso Algorithm ◽  
Fault Isolation ◽  
Voltage Distribution ◽  
Actual Distribution ◽  
Operation Conditions ◽  
Network Losses ◽  
Staged Reconstruction

High voltage distribution network is an important link between transmission and middle voltage distribution network. If a high voltage distribution network fault occurs, the economy and reliability of the whole city distribution system will be seriously affected. To solve this problem, a traditional reconstruction optimization model is improved and a 2-staged reconstruction optimization model is proposed based on an improved particle swarm optimization (PSO) algorithm. The first stage reconstruction only considers the economic goal and the second stage reconstruction needs consider the boundary constrains. The 2-staged method considers the economic benefit and the voltage quality simultaneously. It can achieve the multi goals: meeting actual boundary operation conditions, optimizing the combination of network losses and the number of operation switching. The fault isolation and reconfiguration of an actual distribution network system in Dezhou power grid were simulated. Simulation results validate the rationality and feasibility of the proposed method

Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

scholarly journals Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Zhichuan Zhu ◽  
Alin Hou ◽  
Qingdong Zhao ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Pulmonary Nodule ◽  
Multiple Kernel Learning ◽  
Pso Algorithm ◽  
Support Vector ◽  
Training Time ◽  
Inertia Weight ◽  
Fitness Value ◽  
Svm Algorithm ◽  
Fitness Curve

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Volkan Soner Özsoy
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Pso Algorithm ◽  
Complex Problem ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Content Type ◽  
Inertia Weight

Purpose This paper aims to consider each strategy of the particle swarm optimization (PSO) as a unit in data envelopment analysis (DEA) and uses the minimax mixed-integer linear programming DEA approach to find the most suitable inertia weight strategy. A total of 15 inertia weight strategies were empirically examined in a suite of 42 benchmark problems in the view of DEA. Design/methodology/approach PSO is very sensitive to inertia weight strategies, and therefore, an important amount of research attempts has been concentrated on these strategies. There is no research into the determination of the most suitable inertia weight strategy; however, there are a large number of comparisons related to the inertia weight strategies. DEA is one of the performance evaluation methods, and its models classify the set of strategies into two distinct sets as efficient and inefficient. However, only one of the strategies should be used in the PSO algorithm. Some effective models were proposed to find the most efficient strategy. Findings The experimental studies demonstrate that an approach is a useful tool in the determination of the most suitable strategy. Besides, if the author encounters a new complex problem whose properties are known, it will help the author to choose the best strategy. Practical implications A heavy oil thermal cracking three lumps model for the simplification of the reaction system was used because it is an important complicated chemical process. In addition, the soil water retention curve (SWRC) plays an important role in diverse facets of agricultural engineering. As the SWRC can be regarded as a nonlinear function between the water content and the soil water potential, Van Genuchten model is proposed to describe this function. To determinate these model parameters, an optimization problem is formulated, which minimizes the difference between the measured and modeled data. Originality/value In this paper, the PSO algorithm is integrated with minimax mixed-integer linear programming to find the most suitable inertia weight strategy. In this way, the best strategy could be chosen for a new more complex problem.

An Improved Particle Swarm Optimization Algorithm with Adaptive Inertia Weights

2019 ◽  
Vol 18 (03) ◽  
pp. 833-866 ◽  
Author(s):  
Mi Li ◽  
Huan Chen ◽  
Xiaodong Wang ◽  
Ning Zhong ◽  
Shengfu Lu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Random Mutation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Fitness Value ◽  
The One ◽  
Individual Particles

The particle swarm optimization (PSO) algorithm is simple to implement and converges quickly, but it easily falls into a local optimum; on the one hand, it lacks the ability to balance global exploration and local exploitation of the population, and on the other hand, the population lacks diversity. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. This method increases the diversity of inertia weights and is conducive to balancing the capabilities of global exploration and local exploitation. (2) A mutation threshold is used to determine which particles need to be mutated. This method compensates for the inaccuracy of random mutation, effectively increasing the diversity of the population. To evaluate the performance of the proposed AIWPSO algorithm, benchmark functions are used for testing. The results show that AIWPSO achieves satisfactory results compared with those of other PSO algorithms. This outcome shows that the AIWPSO algorithm is conducive to balancing the abilities of the global exploration and local exploitation of the population, while increasing the diversity of the population, thereby significantly improving the optimization ability of the PSO algorithm.

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