scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

scholarly journals Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution

2021 ◽  
Vol 7 ◽  
pp. e419
Author(s):  
Jesus Hernandez-Barragan ◽  
Carlos Lopez-Franco ◽  
Nancy Arana-Daniel ◽  
Alma Y. Alanis
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Mobile Manipulators ◽  
Tracking Tasks ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Joint Limits ◽  
Self Adaptive

This article presents an approach to solve the inverse kinematics of cooperative mobile manipulators for coordinate manipulation tasks. A self-adaptive differential evolution algorithm is used to solve the inverse kinematics as a global constrained optimization problem. A kinematics model of the cooperative mobile manipulators system is proposed, considering a system with two omnidirectional platform manipulators with n DOF. An objective function is formulated based on the forward kinematics equations. Consequently, the proposed approach does not suffer from singularities because it does not require the inversion of any Jacobian matrix. The design of the objective function also contains penalty functions to handle the joint limits constraints. Simulation experiments are performed to test the proposed approach for solving coordinate path tracking tasks. The solutions of the inverse kinematics show precise and accurate results. The experimental setup considers two mobile manipulators based on the KUKA Youbot system to demonstrate the applicability of the proposed approach.

Design of a Parallel Manipulator Based on Multi-Objective Self-Adaptive Differential Evolution Algorithm

2013 ◽  
Vol 328 ◽  
pp. 3-8 ◽  
Author(s):  
Qing Mei Meng
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Search Space ◽  
Parallel Robot ◽  
Feasible Region ◽  
Multi Objective ◽  
Trial Vector ◽  
Adaptive Differential Evolution ◽  
Optimal Pid Control ◽  
Self Adaptive

In order to improve highly non-isotropic input-output relations in the optimal design of a parallel robot, this paper presents a method based on a multi-objective self-adaptive differential evolution (MOSaDE) algorithm.The approach considers a solution-diversity mechanism coupled with a memory of those sub-optimal solutions found during the process. In theMOSaDE algorithm, both trial vector generation strategies and their associated control parameter values were gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings could be determined adaptively to match different phases of the search processevolution.Furthermore, a constraint-handling mechanism is added to bias the search to the feasible region of the search space. The obtained solution will be a set of optimal geometric parameters and optimal PID control gains. The empirical analysis of thenumerical results shows the efficiency of the proposed algorithm.

Differential Evolution with Self-Adaptation

2011 ◽  
pp. 488-493 ◽  
Author(s):  
Janez Brest
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimization Method ◽  
Control Parameters ◽  
Research Areas ◽  
De Algorithm ◽  
Practical Engineering ◽  
Self Adaptation ◽  
Self Adaptive

Many practical engineering applications can be formulated as a global optimization problem, in which objective function has many local minima, and derivatives of the objective function are unavailable. Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces (Storn & Price, 1997) (Liu & Lampinen, 2005) (Price, Storn & Lampinen, 2005) (Feoktistov, 2006). Nowadays it is used as a powerful global optimization method within a wide range of research areas. Recent researches indicate that self-adaptive DE algorithms are considerably better than the original DE algorithm. The necessity of changing control parameters during the optimization process is also confirmed based on the experiments in (Brest, Greiner, Boškovic, Mernik, Žumer, 2006a). DE with self-adaptive control parameters has already been presented in (Brest et al., 2006a). This chapter presents self-adaptive approaches that were recently proposed for control parameters in DE algorithm.

scholarly journals Optimization of Wave Energy Converter Arrays by an Improved Differential Evolution Algorithm

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3522 ◽  
Author(s):  
Hong-Wei Fang ◽  
Yu-Zhu Feng ◽  
Guo-Ping Li
Keyword(s):  
Differential Evolution ◽  
Incident Wave ◽  
Wave Energy ◽  
Differential Evolution Algorithm ◽  
Wave Energy Converter ◽  
Wave Number ◽  
Adaptive Mutation ◽  
Energy Converter ◽  
De Algorithm ◽  
Evolution Algorithm

Since different incident waves will cause the same array to perform differently with respect to the wave energy converter (WEC), the parameters of the incident wave, including the incident angle and the incident wave number, are taken into account for optimizing the wave energy converter array. Then, the differential evolution (DE) algorithm, which has the advantages of simple operation procedures and a strong global search ability, is used to optimize the wave energy converter array. However, the traditional differential evolution algorithm cannot satisfy the convergence precision and speed simultaneously. In order to make the optimization results more accurate, the concept of an adaptive mutation operator is presented to improve the performance of differential evolution algorithm. It gives the improved algorithm a faster convergence and a higher precision ability. The three-float, five-float, and eight-float arrays were optimized, respectively. It can be concluded that the larger the size of the array is, the greater the interaction between the floats is. Hence, a higher efficiency of wave energy extraction for wave energy converter arrays is achieved by the layout optimization of the array of wave energy converters. The results also show that the optimal layout of the array system is inhomogeneously distributed and that the improved DE algorithm used on array optimization is superior to the traditional DE algorithm.

Application of Differential Evolution Algorithm for Calibrating Design Seismic Response Spectra

2012 ◽  
Vol 229-231 ◽  
pp. 1886-1890
Author(s):  
Jing Yan Lan ◽  
Yue Jun Lu
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Seismic Response ◽  
Turning Point ◽  
Differential Evolution Algorithm ◽  
Response Spectra ◽  
Optimization Methods ◽  
Characteristic Parameters ◽  
Great Ability ◽  
De Algorithm

Differential evolution (DE) algorithm is a heuristic approach that has a great ability to solve complex optimization and converges faster and with more certainty than many other acclaimed global optimization methods. In this paper, DE algorithm is applied to calibrate design seismic response spectra, the basic idea is that design response spectra and calculated response spectra are defined as objective function of characteristic parameters, the first turning point, the second turning point (characteristic period), the platform height and the attenuation index, of design response spectra, and the characteristic parameters are obtained by minimizing the objective function. Three pieces of the calculated response spectra for one real are calibrated as an application example. The obtained results demonstrate the effectiveness and efficiency of the proposed method.

scholarly journals Two Strategies to Improve the Differential Evolution Algorithm for Optimizing Design of Truss Structures

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ching-Yun Kao ◽  
Shih-Lin Hung ◽  
Budy Setiawan
Keyword(s):  
Differential Evolution ◽  
Random Search ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Premature Convergence ◽  
Truss Structures ◽  
Adaptive Mutation ◽  
Exploration And Exploitation ◽  
De Algorithm ◽  
Mutation Strategies

The performance of differential evolution (DE) mostly depends on mutation operator. Inappropriate configurations of mutation strategies and control parameters can cause stagnation due to over exploration or premature convergence due to over exploitation. Balancing exploration and exploitation is crucial for an effective DE algorithm. This work presents an enhanced DE (EDE) for truss design that utilizes two new strategies, namely, integrated mutation and adaptive mutation factor strategies, to obtain a good balance between the exploration and exploitation of DE. Three mutation strategies (DE/rand/1, DE/best/2, and DE/rand-to-best/1) are combined in the integrated mutation strategy to increase the diversity of random search and avoid premature convergence to a local minimum. The adaptive mutation factor strategy systematically adapts the mutation factor from a large value to a small value to avoid premature convergence in the early searching period and to increase convergence to the global optimum solution in the later searching period. The outstanding performance of the proposed EDE is demonstrated through optimization of five truss structures.

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