A Inflexion Nonlinear Global Particle Swarm Optimization (PSO) Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1582-1585
Author(s):  
Li Xia Lv ◽  
Xiang Yu Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Pressure Control ◽  
Temperature Control System ◽  
Swarm Optimization ◽  
Position Information ◽  
Global Average ◽  
Long Time

According to the question of the standard particle swarm optimization (PSO) algorithm is prone to premature and no convergence phenomenon, this paper proposed an algorithm of Inflection nonlinear global PSO. The algorithm introduces nonlinear trigonometric factor and the global average location information in the formula of velocity updating. It take advantage of the convex of the triangle function cause the particles early in the larger velocity search maintain long time and in the later searching with smaller speed maintain long time, use the global average position information make the population can use more information to update their position. The method are applied in optimizing in the parameters of the main steam temperature control system and furnace pressure control system for comparison, the results show that the method in the search speed and precision than standard PSO has significantly improved.

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.

scholarly journals Reaction Control System Optimization for Maneuverable Reentry Vehicles Based on Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Hang Gui ◽  
Ruisheng Sun ◽  
Wei Chen ◽  
Bin Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
System Optimization ◽  
Suboptimal Control ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Reentry Vehicles ◽  
Reaction Control System

This paper presents a new parametric optimization design to solve a class of reaction control system (RCS) problem with discrete switching state, flexible working time, and finite-energy control for maneuverable reentry vehicles. Based on basic particle swarm optimization (PSO) method, an exponentially decreasing inertia weight function is introduced to improve convergence performance of the PSO algorithm. Considering the PSO algorithm spends long calculation time, a suboptimal control and guidance scheme is developed for online practical design. By tuning the control parameters, we try to acquire efficacy as close as possible to that of the PSO-based solution which provides a reference. Finally, comparative simulations are conducted to verify the proposed optimization approach. The results indicate that the proposed optimization and control algorithm has good performance for such RCS of maneuverable reentry vehicles.

Particle Swarm vs. Evolutionary Optimization Techniques in a Multiobjective Framework for Damage Identification

2009 ◽  
Vol 413-414 ◽  
pp. 661-668
Author(s):  
Ricardo Perera ◽  
Sheng En Fang ◽  
Antonio Ruiz
Keyword(s):  
Particle Swarm Optimization ◽  
Damage Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Identification Problems ◽  
Swarm Optimization ◽  
The Family ◽  
Long Time ◽  
Evolutionary Optimization Techniques

In the context of real-world damage detection problems, the lack of a clear objective function advises to perform simultaneous optimizations of several objectives with the purpose of improving the performance of the procedure. Evolutionary algorithms have been considered to be particularly appropriate to these kinds of problems. However, evolutionary techniques require a relatively long time to obtain a Pareto front of high quality. Particle swarm optimization (PSO) is one of the newest techniques within the family of optimization algorithms. The PSO algorithm relies only on two simple PSO self-updating equations whose purpose is to try to emulate the best global individual found, as well as the best solutions found by each individual particle. Since an individual obtains useful information only from the local and global optimal individuals, it converges to the best solution quickly. PSO has become very popular because of its simplicity and convergence speed. However, there are many associated problems that require further study for extending PSO in solving multi-objective problems. The goal of this paper is to present the first application of PSO to multiobjective damage identification problems and investigate the applicability of several variations of the basic PSO technique. The potential of combining evolutionary computation and PSO concepts for damage identification problems is explored in this work by using a multiobjective evolutionary particle swarm optimization algorithm.

Particle Swarm Optimization of PIλDμ Control of Heating Furnace Temperature Control System

2014 ◽  
Vol 721 ◽  
pp. 205-209
Author(s):  
Pei Guang Wang ◽  
Lian Zhang ◽  
Xiao Ping Zong
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Fractional Order ◽  
Temperature Control ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Heating Furnace ◽  
Temperature Control System ◽  
Swarm Optimization

Due to the complexity of the heat transfer for heating furnace, some characteristics are caused such as big inertia, great lag. In the temperature control system for heating furnace, the traditional PID controller can not get satisfactory effect, that dynamic is instability and control accuracy is bad, which is very detrimental to the system to achieve optimum efficiency. A fractional order PIλDμ controller based on particle swarm optimization method was designed, at the same time compared with PID control. Simulation results show that, fractional order PIλDμ control based on particle swarm optimization has better convergence stability, faster response times and higher accuracy value. Fractional order PIλDμ controller has better dynamic performance, compared with traditional PID controller, greatly improves the quality control system.

PID Control of Main Steam Temperature Used Quantum Particle Swarm Optimization

2012 ◽  
Vol 591-593 ◽  
pp. 1204-1207
Author(s):  
Yan Min Nie ◽  
Tao Wang ◽  
Ying Bo An
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Temperature Control ◽  
Particle Swarm ◽  
Temperature Control System ◽  
Particle Swarm Algorithm ◽  
Steam Temperature ◽  
Swarm Optimization ◽  
Boiler Operation ◽  
Main Steam

The main steam temperature is always an important indicator of the boiler operation quality, high or low will affect the quality of boiler operation. At first, introduce a algorithm PSO, which can used to optimize the PID parameters of a main steam temperature control system. Then, improved the PSO, and studied a kind of improved particle swarm algorithm—quantum apply quantum-behaved particle swarm optimization (QPSO). And this algorithm is used to optimize the PID parameters of a main steam temperature control system, got the best parameters. In the end, simulation result shows that, compared with basic particle swarm optimization (PSO),QPSO can make main steam temperature control system has a better control of quality, and improves the system of static and dynamic characteristics.

scholarly journals OPTIMIZATION OF PID PARAMETERS BASED ON PARTICLE SWARM OPTIMIZATION FOR BALL AND BEAM SYSTEM

2020 ◽  
Vol 5 (9) ◽  
pp. 59-69
Author(s):  
Haytem Ali ◽  
Abdulgani Albagul ◽  
Alhade Algitta
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Trial And Error ◽  
Swarm Optimization ◽  
Beam Control ◽  
Trial And Error Method ◽  
Error Method

This paper introduces the application of an optimization technique, known as Particle Swarm Optimization (PSO) algorithm to the problem of tuning the Proportional-Integral-Derivative (PID) controller for a linearized ball and beam control system. After describing the basic principles of the Particle Swarm Optimization, the proposed method concentrates on finding the optimal solution of PID controller in the cascade control loop of the Ball and Beam Control System. Ball and Beam control system tends to balance a ball on a particular position on the beam as defined by the user. The efficiency of Particle Swarm Optimization algorithm for tuning the controller will be compared with a classical method, Trial and Error method. The comparison is based on the time response performance. The two tuning methods have been developed by simulation study using Matlab\ m-file software. The evaluations show that Evolutionary method Particle Swarm Optimization (PSO) algorithm gives a much better response than trial and error method.

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