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.

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 Using particle swarm optimization to solve test functions problems

2021 ◽  
Vol 10 (6) ◽  
pp. 3422-3431
Author(s):  
Issa Ahmed Abed ◽  
May Mohammed Ali ◽  
Afrah Abood Abdul Kadhim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Standard Deviation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Speed Of Convergence ◽  
Test Functions ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Engineering Problems

In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.

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.

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 GPU-Based Parallel Particle Swarm Optimization Methods for Graph Drawing

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Jianhua Qu ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Feng Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Graph Drawing ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Population Based ◽  
Pictorial Representation ◽  
Swarm Optimization ◽  
Wide Range

Particle Swarm Optimization (PSO) is a population-based stochastic search technique for solving optimization problems, which has been proven to be effective in a wide range of applications. However, the computational efficiency on large-scale problems is still unsatisfactory. A graph drawing is a pictorial representation of the vertices and edges of a graph. Two PSO heuristic procedures, one serial and the other parallel, are developed for undirected graph drawing. Each particle corresponds to a different layout of the graph. The particle fitness is defined based on the concept of the energy in the force-directed method. The serial PSO procedure is executed on a CPU and the parallel PSO procedure is executed on a GPU. Two PSO procedures have different data structures and strategies. The performance of the proposed methods is evaluated through several different graphs. The experimental results show that the two PSO procedures are both as effective as the force-directed method, and the parallel procedure is more advantageous than the serial procedure for larger graphs.

scholarly journals Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

2021 ◽  
Vol 12 (2) ◽  
pp. 736
Author(s):  
Kareem G. Abdulhussein ◽  
Naseer M. Yasin ◽  
Ihsan J. Hasan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Modern Method ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Permanent Magnet Dc Motor ◽  
Tracking Trajectory

In this paper, two optimization methods are used to adjust the gain values for the cascade PID controller. These algorithms are the butterfly optimization algorithm (BOA), which is a modern method based on tracking the movement of butterflies to the scent of a fragrance to reach the best position and the second method is particle swarm optimization (PSO). The PID controllers in this system are used to control the position, velocity, and current of a permanent magnet DC motor (PMDC) with an accurate tracking trajectory to reach the desired position. The simulation results using the Matlab environment showed that the butterfly optimization algorithm is better than the particle swarming optimization (PSO) in terms of performance and overshoot or any deviation in tracking the path to reach the desired position. While an overshoot of 2.557% was observed when using the PSO algorithm, and a position deviation of 7.82 degrees was observed from the reference position.

scholarly journals Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

2020 ◽  
Vol 45 (3) ◽  
pp. 233-254
Author(s):  
Mateusz Zaborski ◽  
Michał Okulewicz ◽  
Jacek Mańdziuk
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Quadratic Function ◽  
Sampling Strategy ◽  
Large Degree ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Optimization Framework ◽  
Model Based

AbstractThis paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper.We investigate the features of two model-based optimizers: one utilizing a quadratic function and the other one utilizing a polynomial function. We analyze the conditions under which those model-based approaches provide an effective sampling strategy. Proposed model-based optimizers are evaluated on the functions from the COCO BBOB benchmark set.

Particle Swarm Optimization Algorithm Based on Escape Boundary

2011 ◽  
Vol 361-363 ◽  
pp. 1426-1431
Author(s):  
Wen Hua Han
Keyword(s):  
Boundary Condition ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Population Based ◽  
Global Optimum ◽  
Swarm Optimization

The particle swarm optimization (PSO) is a population-based stochastic evolutionary algorithm, noted for its capability of searching for the global optimum of complex problems. Particles flying out of the solution space will lead to invalid solutions. So often in engineering applications, boundary condition is used to confine the particles within the solution space. In this paper, a new boundary is proposed, which is called as escape boundary. The solution space is divided into three sections, that is, the inside，escape boundary and the outside of the boundary. The location of the global solution in the solution space, accordingly has two types, that is, the global optimum around the center of the solution space, and the global optimum close to the escape boundary. The proposed boundary is introduced into the PSO algorithm, and is compared to the damping boundary. The experimental results show that the PSO based on escape boundary has better search ability and faster convergence rate.

An Efficient Particle Swarm Optimization with Multidimensional Mean Learning

2020 ◽  
pp. 2151005
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Junhui Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Population Based ◽  
Swarm Optimization ◽  
Complex Problems ◽  
Opposition Based Learning

Particle swarm optimization (PSO) algorithm is a stochastic and population-based optimization algorithm. Its traditional learning strategy is implemented by updating the best position using the particle’s own historical best experience and its neighborhood’s best experience to find the optimal solution of the problem. However, the learning strategy is ineffective when dealing with highly complex problems. In this paper, a particle swarm optimization algorithm based on a multidimensional mean learning strategy is proposed. In this algorithm, an opposition-based learning strategy is utilized to initialize the population to enhance the exploitation capability. Furthermore, the historical best positions of all the particles are reconstructed in a vertical crossover manner that is based on the mean information of multiple optimal dimensions to generate the guiding particles. Additionally, an improved inertia weight is used to further guide all the particle movements to balance the capability of the proposed algorithm for global exploration and local exploitation. The proposed algorithm is tested on 12 benchmark functions and is compared with some well-known PSO algorithms. The experimental results show that the proposed algorithm obtains more competitive optimal solution compared with other PSO algorithms when solving high-dimensional complex problems.

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