Genetic algorithm approach with an adaptive search space based on EM algorithm in two-component mixture Weibull parameter estimation

2020 ◽  
Author(s):  
Muhammet Burak Kılıç ◽  
Yusuf Şahin ◽  
Melih Burak Koca
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Em Algorithm ◽  
Search Space ◽  
Adaptive Search ◽  
Weibull Parameter ◽  
Component Mixture ◽  
Two Component ◽  
Genetic Algorithm Approach

Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

Hybrid Optimization Algorithm Using Enhanced Continuous Tabu Search and Genetic Algorithm for Parameter Estimation

2006 ◽  
Author(s):  
Barathram Ramkumar ◽  
Marco P. Schoen ◽  
Feng Lin ◽  
Brian G. Williams
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Tabu Search ◽  
Search Space ◽  
The Novel ◽  
Search Spaces ◽  
Estimation Algorithms ◽  
Hybrid Optimization Algorithm ◽  
Estimation Problems ◽  
Promising Area

A new algorithm using Enhanced Continuous Tabu Search (ECTS) and genetic algorithm (GA) is proposed for parameter estimation problems. The proposed algorithm combines the respective strengths of ECTS and GA. The ECTS is a modified Tabu Search (TS), which has good search capabilities for large search spaces. In this work, the ECTS is used to define smaller search spaces, which are used in a second stage by a GA to find the respective local minima. The ECTS covers the global search space by using a TS concept called diversification and then selects the most promising regions in the search space. Once the promising areas in the search space are identified, the proposed algorithm employs another TS concept called intensification in order to search the promising area thoroughly. The proposed algorithm is tested with benchmark multimodal functions for which the global minimum is known. In addition, the novel algorithm is used for parameter estimation problems, where standard estimation algorithms encounter problems estimating the parameters in an un-biased fashion. The simulation results indicate the effectiveness of the proposed hybrid algorithm.

An adaptive niching genetic algorithm approach for generating multiple solutions of serial manipulator inverse kinematics with applications to modular robots

Robotica ◽  
2009 ◽  
Vol 28 (4) ◽  
pp. 493-507 ◽  
Author(s):  
Saleh Tabandeh ◽  
William W. Melek ◽  
Christopher M. Clark
Keyword(s):  
Genetic Algorithm ◽  
Multiple Solutions ◽  
Inverse Kinematics ◽  
Search Space ◽  
Modular Robots ◽  
Modified Genetic Algorithm ◽  
Niching Methods ◽  
Genetic Algorithm Approach ◽  
Reconfigurable Robot ◽  
Niching Method

SUMMARYInverse kinematics (IK) is a nonlinear problem that may have multiple solutions. A modified genetic algorithm (GA) for solving the IK of a serial robotic manipulator is presented. The algorithm is capable of finding multiple solutions of the IK through niching methods. Despite the fact that the number and position of solutions in the search space depends on the position and orientation of the end-effector as well as the kinematic configuration (KC) of the robot, the number of GA parameters that must be set by a user are limited to a minimum through the use of an adaptive niching method. The only requirement of the algorithm is the forward kinematics (FK) equations which can be easily obtained from the Denavit–Hartenberg link parameters and joint variables of the robot. For identifying and processing the outputs of the proposed GA, a modified filtering and clustering phase is also added to the algorithm. For the postprocessing stage, a numerical IK solver is used to achieve convergence to the desired accuracy. The algorithm is validated on three KCs of a modular and reconfigurable robot (MRR).

scholarly journals Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

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