Optimum Vibration Design of Fiber Metal Laminated Panels by Particle Swarm Optimization Algorithm

2011 ◽  
Author(s):  
Mohammad Homayoun Sadr ◽  
Hadi Ghashochi Bargh ◽  
Hoofar Pourzand ◽  
Masoud Karimi
Keyword(s):  
Particle Swarm Optimization ◽  
Plate Theory ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Good Efficiency ◽  
Design Variables ◽  
Laminated Panels ◽  
Fiber Metal

This paper illustrates the application of the particle swarm optimization (PSO) algorithm to the lay-up design of symmetrically fiber metal laminated panels for maximization of fundamental frequency. The design variables are the number of layers, the fiber orientation angles of inner composite layers, edge conditions and panel length/width ratios. The classical laminated plate theory (CLPT) is used to calculate the natural frequencies and the fitness function is computed with a semi-analytical finite strip method which has been developed on the basis of full energy methods. The performance of the PSO is also compared with the simple genetic algorithm and shows the good efficiency of the PSO algorithm. To check the validity, the results are corroborated by comparing with some other stacking sequences.

A High-Dimensional Model Representation Guided PSO Methodology With Application on Compressor Airfoil Shape Optimization

2016 ◽  
Author(s):  
A. Safari ◽  
K. H. Hajikolaei ◽  
H. G. Lemu ◽  
G. G. Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Based ◽  
Superior Performance ◽  
High Dimensional ◽  
Benchmark Problems ◽  
High Dimensional Model Representation ◽  
Swarm Optimization ◽  
Design Variables

Although metaheuristic techniques have recently become popular in optimization, still they are not suitable for computationally expensive real-world problems, specifically when the problems have many input variables. Among these techniques, particle swarm optimization (PSO) is one of the most well-known population-based nature-inspired algorithms which can intelligently search huge spaces of possible arrangements of design variables to solve various complex problems. The candidate solutions and accordingly the required number of evaluated particles, however, dramatically increase with the number of design variables or the dimension of the problem. This study is a major modification to an original PSO for using all previously evaluated points aiming to increase the computational efficiency. For this purpose, a metamodeling methodology appropriate for so-called high-dimensional, expensive, black-box (HEB) problems is used to efficiently generate an approximate function from all particles calculated during the optimization process. Following the metamodel construction, a term named metamodeling acceleration is added to the velocity update formula in the original PSO algorithm using the minimum of the metamodel. The proposed strategy is called the metamodel guided particle swarm optimization (MGPSO) algorithm. The superior performance of the approach is compared with original PSO using several benchmark problems with different numbers of variables. The developed algorithm is then used to optimize the aerodynamic design of a gas turbine compressor blade airfoil as a challenging HEB problem. The simulation results illustrated the MGPSO’s capability to achieve more accurate results with a considerably smaller number of function evaluations.

scholarly journals Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

Aerospace ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Shuangxi Liu ◽  
Fengping Huang ◽  
Binbin Yan ◽  
Tong Zhang ◽  
Ruifan Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Index System ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Adaptive Pso

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints.

A simple and efficient constrained particle swarm optimization and its application to engineering design problems

2010 ◽  
Vol 224 (2) ◽  
pp. 389-400 ◽  
Author(s):  
T-H Kim ◽  
I Maruta ◽  
T Sugie
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Continuous Type ◽  
Design Variables ◽  
Engineering Design Problems

Engineering optimization problems usually contain various constraints and mixed integer-discrete-continuous type of design variables. This article proposes an efficient particle swarm optimization (PSO) algorithm for such problems. First, the constrained optimization problem is transformed into an unconstrained problem without introducing any problem-dependent or user-defined parameters such as penalty factors or Lagrange multipliers, though such parameters are usually required in general optimization algorithms. Then, the above PSO method is extended to handle integer, discrete, and continuous design variables in a simple manner, yet with a high degree of precision. The proposed PSO scheme is fairly simple and thus it is easy to implement. In order to demonstrate the effectiveness of our method, several mechanical design optimization problems are solved, and the numerical results are compared with those reported in the literature.

scholarly journals Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (8) ◽  
pp. 3417
Author(s):  
Nafis Ahmed ◽  
Chaitali J. Pawase ◽  
KyungHi Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Human Beings ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Area Surveillance

Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

scholarly journals Determining of geometrical characteristic parameters of particle fractal aggregates from light scattering measurement signals

2020 ◽  
pp. 203-203
Author(s):  
Liang Xu ◽  
Zhen-Zong He ◽  
Jun-Kui Mao ◽  
Xing-Si Han
Keyword(s):  
Particle Swarm Optimization ◽  
Light Scattering ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fractal Aggregates ◽  
Swarm Optimization ◽  
Repulsive Particle Swarm Optimization ◽  
Light Scattering Measurement ◽  
Scattering Measurement

Two kind of light scattering measurement methods, i.e. the forward light scattering measurement (FLSM) method and the angular light scattering measurement (ALSM) method, are applied to reconstruct the geometrical morphology of particle fractal aggregates. An improved Attractive and Repulsive Particle Swarm Optimization (IARPSO) algorithm is applied to reconstruct the geometrical structure of fractal aggregates. It has been confirmed to show better convergence properties than the original Particle Swarm Optimization (PSO) algorithm and the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm. Compared with the FLSM method, the ASLM method can obtain more accurate and robust results as the distribution of the fitness function value obtained by the ALSM method is more satisfactory. Meanwhile, the retrieval accuracy can be improved by increasing the number of measurement angles or the interval between adjacent measurement angles even when the random noises are added. All the conclusions have important guiding significance for the further study of the geometry reconstruction experiment of fractal aggregates.

Rekonfigurasi Jaringan Menggunakan Binary Particle Swarm Optimization (BPSO) Pada Penyulang Suryagraha

2020 ◽  
Vol 1 (1) ◽  
pp. 22-30
Author(s):  
Diana Mulya Dewi ◽  
Nuzul Hikmah ◽  
Imam Marzuki ◽  
Ahmad Izzuddin
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electrical Network ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Good Efficiency ◽  
Voltage Loss

A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the endpoint. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses and improve distribution system reliability. Many feeders and buses on the network if calculated manually will be difficult and require a very long time. So it is necessary to solve problems using program assistance. In this case, use Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) algorithm based on the behavior of a herd of insects, such as ants, termites, bees, or birds. BPSO is a development of the PSO algorithm designed to solve the optimization problem in a discrete combination, where the particle takes the value of binary vectors with length n and speed which is defined as the probability of bits to reach value 1. The results show a significant reduction in losses.

Optimization of PMSM Sensorless Control Based on Improved PSO Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 86-92
Author(s):  
Miao Wang Qian ◽  
Guo Jun Tan ◽  
Ning Ning Li ◽  
Zhong Xiang Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Sensorless Control ◽  
Fitness Function ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Extend Kalman Filter ◽  
Swarm Optimization ◽  
Manual Adjustment

For the problem that manual adjustment of the parameters of controller in sensorless control system costs too much time, manpower and always can not get a good result, a new method based on improved particle swarm optimization algorithm is proposed to optimize the parameters. The improved algorithm is based on the standard particle swarm optimization with the simulated annealing algorithm and chaotic search brought in. The speed of motor is estimated by the extend Kalman filter. The error between measured speed and estimated speed of the permanent magnet synchronous motor rotor is used as the fitness function in order that the parameters in the covariance matrix is adjusted.The result of simulation indicates that high estimation precision can be got and the motor represents steadily with few of ripple of the actual speed.With this method, the time of adjustment is reduced and manpower is saved. In addition, the validity of the method is proved in experiment with dSPACE.

An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

2020 ◽  
pp. 2150001
Author(s):  
Wei-Der Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Design Variables ◽  
Constrained Conditions ◽  
Engineering Optimization Problems

Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.

Design on Filter Parameters with Hybrid PSO Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1888-1891
Author(s):  
Yi Zhang ◽  
Ke Wen Xia ◽  
Gen Gu
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Filter Model ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Model Setting

In order to solve the problems in the optimization of filter parameters, such as large amounts of calculation and the complicated mathematical hypotheses, an approach to optimize filter parameters is presented based on the Hybrid Particle swarm optimization (HPSO) algorithm, which includes the establishing of filter model, setting up the fitness-function and optimizing filter parameters by HPSO algorithm. The application example shows that the optimization method improves the design accuracy and saves calculation, and HPSO algorithm is superior to PSO algorithm in optimization of filter parameters.

scholarly journals A New Approach to Sequence Construction With Good Correlation by Particle Swarm Optimization

2015 ◽  
Vol 11 (3) ◽  
pp. 127 ◽  
Author(s):  
Mahdiyar Sarayloo ◽  
Ennio Gambi ◽  
Susanna Spinsante
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Binary Sequence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Binary Sequences ◽  
Swarm Optimization ◽  
Variable Population ◽  
Relationship Of ◽  
The Relationship

In this paper, a novel computationally affordable method to generate long binary sequences featuring desired properties is presented, based on the use of a number of shorter non linear binary sub-sequences. The paper shows the relationship of the Auto- and Cross-Correlation (AC, CC) ofthe generated long binary sequences with the AC and CC ofconstituent sub-sequences. It is also shown that the starting bit position of sub-sequences has an important role on AC and CC of the generated sequences. To generate the optimal long binary sequence from correlation points of view, Particle Swarm Optimization (PSO) algorithm is employed. All the techniques stated in the literature to improve the PSO are implemented and it is clearly shown that the constriction factor and the variable population size turn out to have a great impact on minimizing the fitness function (RMS of AC) representing the target Correlationproperties expected for the resulting long sequence. Possible application scenarios for the long sequences generated by the proposed method are also discussed and evaluated.

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