scholarly journals A fractional-order genetic algorithm for parameter optimization of the moisture movement in a bio-retention system

2019 ◽  
Vol 23 (4) ◽  
pp. 2343-2350
Author(s):  
Xiao-Hua Yang ◽  
Tong Liu ◽  
Yu-Qi Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Parameter Optimization ◽  
Particle Swarm ◽  
Absolute Error ◽  
Moisture Movement ◽  
Swarm Optimization ◽  
System A ◽  
Retention System

A bio-retention system is an important measure for non-point source pollution control. In order to improve the calculation precision for parameter optimization of the moisture movement in a bio-retention system, a real-encoded genetic algorithm based on the fractional-order operation is proposed, in which initial populations are generated by random mapping, and the searching range is automatically renewed with the excellent individuals by fractional-order particle swarm optimization operation. Its efficiency is verified experimentally. The results indicate that the absolute error by the fractional-order operation decreases by 67.73%, 62.23%, and 4.16%, and the relative error decreases by 42.88%, 35.76%, and 6.77%, respectively, compared to those by the standard binary-encoded genetic algorithm, random algorithm, and the particle swarm optimization algorithm. The fractional-order operation has higher precision and it is good for the practical parameter optimization in ecological environment systems.

Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 14 (1) ◽  
pp. 110-118
Author(s):  
Hrvoje Markovic ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Absolute Error ◽  
Population Based ◽  
Test Problems ◽  
Swarm Optimization

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.

A Novel Control Scheme for PWM Boost Converter based on Sliding Mode Control using Particle Swarm Optimization

2020 ◽  
Vol 14 ◽  
Author(s):  
Gang Liu ◽  
Dong Qiu ◽  
Xiuru Wang ◽  
Ke Zhang ◽  
Huafeng Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Steady State ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Mathematic Model ◽  
Absolute Error ◽  
Boost Converter ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Steady State Performance

Background: The PWM Boost converter is a strongly nonlinear discrete system, especially when the input voltage or load varies widely, therefore, tuning the control parameters of which is a challenge work. Objective: In order to overcome the issues, particle swarm optimization (PSO) is employed for tuning the parameters of a sliding mode controller of a boost converter. Methods: Based on the analysis of the Boost converter model and its non-linear characteristics, a mathematic model of a boost converter with a sliding mode controller is built firstly. Then, the parameters of the Boost controller are adjusted based on the integrated time and absolute error (ITAE), integral square error (ISE) and integrated absolute error (IAE) indexes by PSO. Results: Simulation verification was performed, and the results show that the controllers tuned by the three indexes all have excellent robust stability. Conclusion: The controllers tuned by ITAE and ISE indexes have excellent steady-state performance, but the overshoot is large during the startup. The controller tuned by IAE index has better startup performance and slightly worse steady-state performance.

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