scholarly journals Particle Swarm Optimization as an Efficient Computational Method in order to Minimize Vibrations of Multimesh Gears Transmission

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Alexandre Carbonelli ◽  
Joël Perret-Liaudet ◽  
Emmanuel Rigaud ◽  
Alain Le Bot
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Transmission Error ◽  
Computational Method ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Helical Gears ◽  
Great Performance ◽  
Applied Torque ◽  
Static Transmission Error

The aim of this work is to present the great performance of the numerical algorithm of Particle Swarm Optimization applied to find the best teeth modifications for multimesh helical gears, which are crucial for the static transmission error (STE). Indeed, STE fluctuation is the main source of vibrations and noise radiated by the geared transmission system. The microgeometrical parameters studied for each toothed wheel are the crowning, tip reliefs and start diameters for these reliefs. Minimization of added up STE amplitudes on the idler gear of a three-gear cascade is then performed using the Particle Swarm Optimization. Finally, robustness of the solutions towards manufacturing errors and applied torque is analyzed by the Particle Swarm algorithm to access to the deterioration capacity of the tested solution.

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

Introduction of New Anti-dumping Algorithm in EU under WTO Law System Based on Particle Swarm Optimization

2021 ◽  
Vol 7 (5) ◽  
pp. 4558-4567
Author(s):  
Wenwen Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Operation Mode ◽  
Particle Swarm ◽  
Algorithm Analysis ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Law System ◽  
Incomplete System ◽  
Swarm Algorithm ◽  
The Eu

Objectives: Anti dumping new algorithm is an innovative ability based on the WTO legal system, which has made an important contribution to the economic development of the EU system. Methods: At present, the operation mode of new antidumping algorithm has some defects, such as structure confusion and incomplete system implementation, which affects the development progress of EU economic growth. Results: Based on the above problems, in this paper, particle swarm algorithm is introduced, based on the optimization analysis of the website structure of the new antidumping algorithm, through the independent screening analysis of particle swarm optimization, combining the WTO economy with the EU status theory, Conclusion: the paper obtains the optimized anti-dumping innovation scheme on the basis of particle swarm algorithm analysis, and finally passes the input test. The feasibility of the scheme is established.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 18-29
Author(s):  
Rongrong Li ◽  
Linrun Qiu ◽  
Dongbo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Bottom Layer ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Elite Group ◽  
Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

scholarly journals Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Wang ◽  
Yongqiang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Correlation Algorithm ◽  
Annealing Algorithm

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

Diagnosis for Vibration Fault of Steam Turbine Based on Modified Particle Swarm Optimization Support Vector Machine

2011 ◽  
Vol 128-129 ◽  
pp. 113-116 ◽  
Author(s):  
Zhi Biao Shi ◽  
Quan Gang Song ◽  
Ming Zhao Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

Due to the influence of artificial factor and slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a modified particle swarm optimization support vector machine (MPSO-SVM). A Steam turbine vibration fault diagnosis model was established and the failure data was used in fault diagnosis. The results of application show the model can get automatic optimization about the related parameters of support vector machine and achieve the ideal optimal solution globally. MPSO-SVM strategy is feasible and effective compared with traditional particle swarm optimization support vector machine (PSO-SVM) and genetic algorithm support vector machine (GA-SVM).

The Research of Particle Swarm Algorithm Based on Heuristic Rules for the Layout of Airplane Equipment Cabin

2013 ◽  
Vol 300-301 ◽  
pp. 659-663
Author(s):  
Xiao Jian Han ◽  
Xiang Fang Ding ◽  
Chun Xiao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Three Dimensions ◽  
Small Scale ◽  
Particle Swarm Algorithm ◽  
Heuristic Rules ◽  
Layout Problem ◽  
Swarm Optimization ◽  
Np Problem

How to get the most optimal solution of equipment layout in the aircraft cabin of the limited space is a completely NP problem. The problem is abstracted as three dimensions （3D） layout problem. A co-evolutionary particle swarm optimization with heuristic rules is presented. The cabin is decomposed into several small-scale layout problems. The co-evolutionary framework is adopted, and particle swarm optimization (PSO) and heuristic roles for layout are integrated to solve this problem. Finally, an example is used to verify the feasibility and effectiveness of the algorithm.

Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

2013 ◽  
Vol 475-476 ◽  
pp. 956-959 ◽  
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

scholarly journals Improvement of Imperialist Competitive Algorithm based on the Cosine Similarity Criterion of Neighboring Objects

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Maryam Houtinezhad ◽  
Hamid Reza Ghaffary
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Imperialist Competitive Algorithm ◽  
Cosine Similarity ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Competitive Algorithm ◽  
Combined Algorithm

The goal of optimizing the best acceptable answer is according to the limitations and needs of the problem. For a problem, there are several different answers that are defined to compare them and select an optimal answer; a function is called a target function. The choice of this function depends on the nature of the problem. Sometimes several goals are together optimized; such optimization problems are called multi-objective issues. One way to deal with such problems is to form a new objective function in the form of a linear combination of the main objective functions. In the proposed approach, in order to increase the ability to discover new position in the Imperialist Competitive Algorithm (ICA), its operators are combined with the particle swarm optimization. The colonial competition optimization algorithm has the ability to search global and has a fast convergence rate, and the particle swarm algorithm added to it increases the accuracy of searches. In this approach, the cosine similarity of the neighboring countries is measured by the nearest colonies of an imperialist and closest competitor country. In the proposed method, by balancing the global and local search, a method for improving the performance of the two algorithms is presented. The simulation results of the combined algorithm have been evaluated with some of the benchmark functions. Comparison of the results has been evaluated with respect to metaheuristic algorithms such as Differential Evolution (DE), Ant Lion Optimizer (ALO), ICA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

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