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 A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Waqas Haider Bangyal ◽  
Abdul Hameed ◽  
Wael Alosaimi ◽  
Hashem Alyami
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Critical Factor ◽  
Pso Algorithm ◽  
Population Based ◽  
Test Problems ◽  
Accuracy Score ◽  
Swarm Optimization ◽  
Function Integration

Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique used to search for food with the intrinsic manner of bee swarming. PSO is widely used to solve the diverse problems of optimization. Initialization of population is a critical factor in the PSO algorithm, which considerably influences the diversity and convergence during the process of PSO. Quasirandom sequences are useful for initializing the population to improve the diversity and convergence, rather than applying the random distribution for initialization. The performance of PSO is expanded in this paper to make it appropriate for the optimization problem by introducing a new initialization technique named WELL with the help of low-discrepancy sequence. To solve the optimization problems in large-dimensional search spaces, the proposed solution is termed as WE-PSO. The suggested solution has been verified on fifteen well-known unimodal and multimodal benchmark test problems extensively used in the literature, Moreover, the performance of WE-PSO is compared with the standard PSO and two other initialization approaches Sobol-based PSO (SO-PSO) and Halton-based PSO (H-PSO). The findings indicate that WE-PSO is better than the standard multimodal problem-solving techniques. The results validate the efficacy and effectiveness of our approach. In comparison, the proposed approach is used for artificial neural network (ANN) learning and contrasted to the standard backpropagation algorithm, standard PSO, H-PSO, and SO-PSO, respectively. The results of our technique has a higher accuracy score and outperforms traditional methods. Also, the outcome of our work presents an insight on how the proposed initialization technique has a high effect on the quality of cost function, integration, and diversity aspects.

scholarly journals Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 174 ◽  
Author(s):  
Hongli Guo ◽  
Bin Li ◽  
Wei Li ◽  
Fengjuan Qiao ◽  
Xuewen Rong ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Network Size ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Learning Machine ◽  
Hidden Layer ◽  
Configuration Simulation

We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness.

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 Using Particle Swarm Optimization as Pathfinding Strategy in a Space with Obstacles

2021 ◽  
Author(s):  
David
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Based ◽  
Target Point ◽  
Adaptive Optimization ◽  
Swarm Optimization ◽  
Algorithm Performance ◽  
Long Time

Particle swarm optimization (PSO) is a search algorithm based on stochastic and population-based adaptive optimization. In this paper, a pathfinding strategy is proposed to improve the efficiency of path planning for a broad range of applications. This study aims to investigate the effect of PSO parameters (numbers of particle, weight constant, particle constant, and global constant) on algorithm performance to give solution paths. Increasing the PSO parameters makes the swarm move faster to the target point but takes a long time to converge because of too many random movements, and vice versa. From a variety of simulations with different parameters, the PSO algorithm is proven to be able to provide a solution path in a space with obstacles.

Optimization of NARX Neural Models Using Particle Swarm Optimization and Genetic Algorithms Applied to Identification of Photovoltaic Systems

2021 ◽  
Vol 143 (5) ◽  
Author(s):  
Ronnyel Carlos Cunha Silva ◽  
José Maria Pires de Menezes Júnior ◽  
José Medeiros de Araújo Júnior
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Mathematical Models ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Superior Performance ◽  
Photovoltaic Systems ◽  
Neural Models ◽  
Swarm Optimization ◽  
Learning Machine

Abstract In this study, genetic algorithms (GAs) and particle swarm optimization (PSO) are used to make an automated choice of hyperparameters of the multilayer perceptron (MLP)-NARX, extreme learning machine (ELM)-NARX, and echo state network (ESN)-NARX neural models applied to the identification of two photovoltaic systems: one installed in Teresina, in Brazil, and another in Hamburg, Germany. The automatic optimization process results showed that the PSO algorithm presents superior performance compared to the GA algorithm. Likewise, the identification carried out aimed to estimate the power generated by photovoltaic systems from two different approaches: linear mathematical models and neural identification models. Thus, the neural models implemented are more efficient and accurate than the linear mathematical models compared. From accuracy, the neural models ESN-NARX and MLP-NARX were considered the best in identifying Hamburg and Teresina’s photovoltaic systems, respectively.

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.

scholarly journals Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Joonwoo Lee ◽  
Won Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Particle Swarm ◽  
Optimization Method ◽  
High Dimensional ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Optima ◽  
One Step ◽  
Bare Bones

This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.

scholarly journals Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships

2020 ◽  
Vol 10 (20) ◽  
pp. 7314
Author(s):  
Mutaz Ryalat ◽  
Hazem Salim Damiri ◽  
Hisham ElMoaqet
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Population Based ◽  
Dynamic Positioning ◽  
Swarm Optimization ◽  
Integral Action ◽  
Engineering Problems ◽  
Control Research

Dynamic positioning (DP) control system is an essential module used in offshore ships for accurate maneuvering and maintaining of ship’s position and heading (fixed location or pre-determined track) by means of thruster forces being generated by controllers. In this paper, an interconnection and damping assignment-passivity based control (IDA-PBC) controller is developed for DP of surface ships. The design of the IDA-PBC controller involves a dynamic extension utilizing the coordinate transformation which adds damping to some coordinates to ensure asymptotic stability and adds integral action to enhance the robustness of the system against disturbances. The particle swarm optimization (PSO) technique is one of the the population-based optimization methods that has gained the attention of the control research communities and used to solve various engineering problems. The PSO algorithm is proposed for the optimization of the IDA-PBC controller. Numerical simulations results with comparisons illustrate the effectiveness of the new PSO-tuned dynamic IDA-PBC controller.

scholarly journals A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA)

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ying-Hui Jia ◽  
Jun Qiu ◽  
Zhuang-Zhuang Ma ◽  
Fang-Fang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Particle Swarm ◽  
Population Based ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Optimization Efficiency ◽  
Multimodal Problems

The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.

A Particle Swarm Optimization Approach for Minimizing GD&T Error in Additive Manufactured Parts

2017 ◽  
Vol 7 (3) ◽  
pp. 69-80 ◽  
Author(s):  
Vimal Kumar Pathak ◽  
Amit Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Process Parameters ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Geometrical Accuracy ◽  
Bed Temperature ◽  
Design Variables ◽  
Flatness Error

This paper presents a particle swarm optimization (PSO) approach to improve the geometrical accuracy of additive manufacturing (AM) parts by minimizing geometrical dimensioning and tolerancing (GD&T) error. Four AM process parameters viz. Bed temperature, nozzle temperature, Infill, layer thickness are taken as input while circularity and flatness error in ABS part are taken as response. A mathematical model is developed for circularity and flatness error individually using regression technique in terms of process parameters as design variables. For the optimum search of the AM process parameter values, minimization of circularity and flatness are formulated as multi-objective, multi-variable optimization problem which is optimized using particle swarm optimization (PSO) algorithm and hence improving the geometrical accuracy of the ABS part.

Sign in / Sign up

Export Citation Format

Share Document