scholarly journals Parameter Optimization for Ship Antiroll Gyros

2020 ◽  
Vol 10 (2) ◽  
pp. 661
Author(s):  
Yuanyuan Zhu ◽  
Shijie Su ◽  
Yuchen Qian ◽  
Yun Chen ◽  
Wenxian Tang
Keyword(s):  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Optimal Parameter ◽  
Reduction Rate ◽  
Pattern Search ◽  
Random Waves ◽  
Search Optimization ◽  
Bacteria Foraging Optimization Algorithm ◽  
Parameter Values ◽  
Better Than

Ship antiroll gyros are a type of equipment used to reduce ships’ roll angle, and their parameters are related to the parameters of a ship and wave, which affect gyro performance. As an alternative framework, we designed a calculation method for roll reduction rate and considered random waves to establish a gyro parameter optimization model, and we then solved it through the bacteria foraging optimization algorithm (BFOA) and pattern search optimization algorithm (PSOA) to obtain optimal parameter values. Results revealed that the two methods could effectively reduce the overall mass and floor space of the antiroll gyro and improved its antirolling effect. In addition, the convergence speed and antirolling effect of the BFOA were better than that of the PSOA.

Evolving a Nelder–Mead Algorithm for Optimization with Genetic Programming

2017 ◽  
Vol 25 (3) ◽  
pp. 351-373 ◽  
Author(s):  
Iztok Fajfar ◽  
Janez Puhan ◽  
Árpád Bűrmen
Keyword(s):  
Genetic Programming ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Original Method ◽  
Quadratic Functions ◽  
Search Optimization ◽  
Standard Set ◽  
Downhill Simplex ◽  
Nelder Mead Algorithm ◽  
Better Than

We used genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead ( 1965 ). In the training process, we used several ten-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm showed overall better performance than the original Nelder–Mead method on a standard set of test functions. We observed that many parts of the genetically produced algorithm were seldom or never executed, which allowed us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder–Mead method while still performing better than the original method.

scholarly journals Meta optimization of stand management with population-based methods

2018 ◽  
Vol 48 (6) ◽  
pp. 697-708 ◽  
Author(s):  
Xingji Jin ◽  
Timo Pukkala ◽  
Fengri Li
Keyword(s):  
Optimal Parameter ◽  
Population Based ◽  
Thinning Intensity ◽  
Stand Management ◽  
Management Actions ◽  
Size Number ◽  
Management Optimization ◽  
Meta Optimization ◽  
Parameter Values ◽  
Better Than

The amount of different products and services obtained from forests depends on several management decisions such as thinning years, thinning intensity, thinning type, and rotation length. The relationships between management actions and the various outputs obtained from forests are complicated. This makes stand management optimization challenging, especially if the number of simultaneously maximized outputs and the number of optimized variables are high. The direct search method of Hooke and Jeeves (HJ) has been used much in stand management optimization. In recent years, population-based methods have been proposed as an alternative to the HJ method. The performance of a population-based method depends on its parameters such as number iterations and population size (number of solution vectors used in the population-based method). This study used two-level meta optimization to simultaneously optimize the parameters of a population-based method and the management schedule of a stand. Four population-based methods were analysed: differential evolution (DE), particle swarm optimization (PS), evolution strategy optimization (ES), and the method of Nelder and Mead (NM). With optimal parameter values, DE and PS found the best stand management schedules, followed by ES and NM. DE and PS performed better than HJ. Therefore, DE and PS should be used more in forest management and their search algorithms should be further developed.

Parameter Optimization of Local Fuzzy Patterns Based on Fuzzy Contrast Measure for White Blood Cell Texture Feature Extraction

2012 ◽  
Vol 16 (3) ◽  
pp. 412-419 ◽  
Author(s):  
Chastine Fatichah ◽  
◽  
Martin Leonard Tangel ◽  
Muhammad Rahmat Widyanto ◽  
Fangyan Dong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Blood Cell ◽  
White Blood Cell ◽  
Parameter Optimization ◽  
Optimal Parameter ◽  
Extraction Methods ◽  
Accuracy Rate ◽  
Cell Classification ◽  
Contrast Measure ◽  
Parameter Values

The parameter optimization of local fuzzy patterns based on the fuzzy contrast measure is proposed for extracting white blood cell texture. The proposed method obtains the optimal parameter values of the nucleus and cytoplasm region of white blood cell image and the best accuracy rate of white blood cell classification can therefore be achieved. To evaluate the performance of the proposed method, 100 microscopic white blood cell images and the supervised learning method are used for white blood cell classification. Results show that the average accuracy rate of white blood cell classification using local fuzzy pattern features with optimal parameter values of a nucleus and a cytoplasm region is 4% more accurate than with uniform parameter values and is 5–18% more accurate than other feature extraction methods. White blood cell feature extraction is part of the white blood cell classification in an automatic cancer diagnosis that is being developed. In addition, the proposed method can be used to obtain the optimal parameter of local fuzzy patterns for other types of datasets.

The Design of Expert PID Parameter Optimized by Genetic Algorithm

2011 ◽  
Vol 130-134 ◽  
pp. 3091-3094
Author(s):  
Jia Tang Cheng ◽  
Wei Xiong ◽  
Li Ai
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Parameter Tuning ◽  
Expert Pid ◽  
Parameter Values ◽  
Better Than

Aiming at the problems Expert PID parameter tuning for time-consuming, and the results are not necessarily the best. In this paper, genetic algorithm is introduced to the parameter optimization, finally get a set of optimal PID parameter values. In comparison with simulated experiments, the results show that the performance of the Designed to optimize the performance of optimization expert PID controller is better than conventional controller, can achieve good dynamic performance.

scholarly journals Position - Velocity Control for Two PMDC Motors Connected by a Cross-Coupling Technique with Butterflies Optimization Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 31-44
Author(s):  
Kareem Ghazi Abdulhussein ◽  
Naseer M. Yasin ◽  
Ihsan J. Hasan
Keyword(s):  
Optimization Algorithm ◽  
Optimal Parameter ◽  
Cross Coupling ◽  
Absolute Error ◽  
Velocity Control ◽  
Coupling Technique ◽  
Dc Motors ◽  
Working Together ◽  
Permanent Magnet Dc Motors ◽  
Parameter Values

In this paper, two contributions are presented. the first is to design two cascade controllers to control the velocity and position for two Permanent Magnet DC motors (PMDC) working together at the same time for use in many applications such as CNC machines, robotics, and others. Furthermore, the cross-coupling technique is used to connect these motors and adjust the precise synchronization of their movement on the axes. The second contribution is the use of the butterfly’s optimization algorithm (BOA) with the objective function Integral Time Absolute Error (ITAE) to extract the optimal parameter values for the two cascade controllers and the synchronization controller in order to obtain the best accurate results. The simulation results showed high accuracy to reach the desired position at a regular velocity of both the PMDC motors with accurate synchronization and tracking trajectory on the axes. In addition, a very small position deviation of 0.021 rad was observed, and the system returned to a steady-state after 2 seconds of applying the full load.

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