scholarly journals A computational method for obtaining Stackelberg solutions to two-level nonlinear programming Problems through particle swarm optimization

2011 ◽  
Vol 23 (3) ◽  
pp. 350-362 ◽  
Author(s):  
Takeshi MATSUI ◽  
Masatoshi SAKAWA ◽  
Keiichi ISHIMARU
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Programming ◽  
Particle Swarm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Stackelberg Solutions

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 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.

Research on Site Selection of Aviation Overhaul Depot Based on Particle Swarm Optimization

2013 ◽  
Vol 274 ◽  
pp. 620-623
Author(s):  
Cheng Liang Liu ◽  
Hui Lin Fan ◽  
Man Yi Hou ◽  
Xue Liang Bao
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Programming ◽  
Support System ◽  
Site Selection ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Swarm Optimization ◽  
Aviation Maintenance ◽  
Nonlinear Programming Method ◽  
Selection Of

As support agencies of the aviation maintenance and support system, the aviation overhaul depots undertake an important and arduous support mission, which play a pivotal role, so the science and rationality of site selection is extremely significant. This paper choose the nonlinear programming method to build a mathematic model, so as to solve the problem. For the purpose of optimizing and solving the model, a thought based on the improved particle swarm optimization which used in the model is put forward to, and an improved PSO which gained by the analyzing the standard PSO and improving the initial PSO is presented in this paper. Finally, an application example is given to analyze and summarize the model.

A Hybrid Particle Swarm Optimization Algorithm Based on Immune Selection for Stochastic Loader Problem

2013 ◽  
Vol 321-324 ◽  
pp. 2214-2218
Author(s):  
Hong Wang ◽  
Pei Yi Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computational Method ◽  
Immune Memory ◽  
Immune Selection ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

The Particle Swarm Optimization Algorithm is a new computational method for the combinatorial optimization problem, it is simple and effective, but it does suffer from the premature convergence. For overcome this problem and finding the optimal solution of the Stochastic Loader problem, we presented a new hybrid Particle Swarm Optimization Algorithm that combines with Artificial Immune Algorithm, such as immune memory, antibody promotion and suppression, immune selection and so on. Numerical example illustrates the higher efficiency and reliability of the new hybrid PSO compared with the basic Particle Swarm Optimization Algorithm.

Design of Temperature Controllor in Diamond Synthetic Chamber Based on Multi-Objective Particle Swarm Optimization

2014 ◽  
Vol 496-500 ◽  
pp. 1448-1451
Author(s):  
Hong Yan Hua ◽  
Cheng Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Programming ◽  
Strong Convergence ◽  
Control Problem ◽  
Temperature Control ◽  
Particle Swarm ◽  
Nonlinear Programming Problem ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

Temperature control problem is a typical multi-constraint/multi-objective nonlinear programming problem in diamond synthetic chamber and it cannot be solved by conventional methods. This paper proposed a constraint multi-objective particle swarm optimization (CMOPSO) algorithm to solve the problem. Simulation results show that the CMOPSO algorithm can be used effectively in diamond temperature control and it has strong convergence optimization abilities for all different parameters and constraint conditions.

Sign in / Sign up

Export Citation Format

Share Document