Control parameter tuning for aircraft crosswind landing via multi-solution particle swarm optimization

2018 ◽  
Vol 50 (11) ◽  
pp. 1914-1925 ◽  
Author(s):  
Qi Bian ◽  
Brett Nener ◽  
Xinmin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Control Parameter ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Swarm Optimization ◽  
Crosswind Landing

Optimization of Drilling Process via Weightless Swarm Algorithm

2015 ◽  
pp. 181-190
Author(s):  
T. O. Ting
Keyword(s):  
Particle Swarm Optimization ◽  
Reduction Rate ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Drilling Process ◽  
Swarm Optimization ◽  
Lower Standard ◽  
Swarm Algorithm ◽  
Novel Algorithm

In this chapter, the main objective of maximizing the Material Reduction Rate (MRR) in the drilling process is carried out. The model describing the drilling process is adopted from the authors' previous work. With the model in hand, a novel algorithm known as Weightless Swarm Algorithm is employed to solve the maximization of MRR due to some constraints. Results show that WSA can find solutions effectively. Constraints are handled effectively, and no violations occur; results obtained are feasible and valid. Results are then compared to previous results by Particle Swarm Optimization (PSO) algorithm. From this comparison, it is quite impossible to conclude which algorithm has a better performance. However, in general, WSA is more stable compared to PSO, from lower standard deviations in most of the cases tested. In addition, the simplicity of WSA offers abundant advantages as the presence of a sole parameter enables easy parameter tuning and thereby enables this algorithm to perform to its fullest.

Parameter Settings in Particle Swarm Optimization

2017 ◽  
pp. 101-132
Author(s):  
Snehal Mohan Kamalapur ◽  
Varsha Patil
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Poor Performance ◽  
Parameter Tuning ◽  
Population Based ◽  
Parameter Control ◽  
Swarm Optimization ◽  
Study Parameter ◽  
Optimal Values ◽  
And Control

The issue of parameter setting of an algorithm is one of the most promising areas of research. Particle Swarm Optimization (PSO) is population based method. The performance of PSO is sensitive to the parameter settings. In the literature of evolutionary computation there are two types of parameter settings - parameter tuning and parameter control. Static parameter tuning may lead to poor performance as optimal values of parameters may be different at different stages of run. This leads to parameter control. This chapter has two-fold objectives to provide a comprehensive discussion on parameter settings and on parameter settings of PSO. The objectives are to study parameter tuning and control, to get the insight of PSO and impact of parameters settings for particles of PSO.

A Culture-Based Particle Swarm Optimization Framework for Dynamic, Constrained Multi-Objective Optimization

2012 ◽  
Vol 3 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Ashwin A. Kadkol ◽  
Gary G. Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Cultural Algorithms ◽  
Swarm Optimization ◽  
Objective Space ◽  
Computationally Intensive ◽  
Available Information

Real-world optimization problems are often dynamic, multiple objective in nature with various constraints and uncertainties. This work proposes solving such problems by systematic segmentation via heuristic information accumulated through Cultural Algorithms. The problem is tackled by maintaining 1) feasible and infeasible best solutions and their fitness and constraint violations in the Situational Space, 2) objective space bounds for the search in the Normative Space, 3) objective space crowding information in the Topographic Space, and 4) function sensitivity and relocation offsets (to reuse available information on optima upon change of environments) in the Historical Space of a cultural framework. The information is used to vary the flight parameters of the Particle Swarm Optimization, to generate newer individuals and to better track dynamic and multiple optima with constraints. The proposed algorithm is validated on three numerical optimization problems. As a practical application case study that is computationally intensive and complex, parameter tuning of a PID (Proportional–Integral–Derivative) controller for plants with transfer functions that vary with time and imposed with robust optimization criteria has been used to demonstrate the effectiveness and efficiency of the proposed design.

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