scholarly journals Application of Differential Evolution in the Solution of Stiff System of Ordinary Differential Equations

2020 ◽  
Vol 8 (1) ◽  
pp. 01-08
Author(s):  
Ashiribo Senapon Wusu ◽  
Olusola Olabanjo ◽  
Benjamin Aribisala

In recent times, the adaptation of evolutionary optimization algorithms for obtaining optimal solutions of many classical problems is gaining popularity. In this paper, optimal approximate solutions of initial--valued stiff system of first--order Ordinary Differential Equation (ODE) are obtained by converting the ODE into constrained optimization problem. The later is then solve via differential evolution algorithm. To illustrate the efficiency of the proposed approach, two numerical examples were considered. This approach showed significant improvement on the accuracy of the results produced compared with existing methods discussed in literature.

2011 ◽  
Vol 308-310 ◽  
pp. 2431-2435 ◽  
Author(s):  
Na Li ◽  
Yuan Xiang Li ◽  
Zhi Guo Huang ◽  
Yong Wang

In multimodal optimization, the original differential evolution algorithm is easy to duplicate and miss points of the optimal value. To solve this problem, a modified differential evolution algorithm, called niche differential evolution (NDE), is proposed. In the algorithm, the basic differential evolution algorithm is improved based on the niche technology. The rationality to construct the proposed algorithm is discussed. Shubert function, a representative multimodal optimization problem is used to verify the algorithm. The results show that the proposed algorithm can find all global optimum points quickly without strict request for parameters, so it is a good approach to find all global optimum points for multimodal functions.


2011 ◽  
Vol 243-249 ◽  
pp. 4642-4646
Author(s):  
Hai Ying Deng ◽  
Zhi Gang Zhang ◽  
Yi Gang Yu

Differential evolution algorithm (differential evolution, DE) is a multi-objective evolutionary algorithm based on groups, which instructs optimization search by swarm intelligence produced by co-operation and competition among individuals within groups. While it can track the dynamics of the current search by the DE specific memory, in order to adjust their search strategy. The strong global convergence and robustness of the characteristics can solve the complex optimization problem which it hardly solves with the mathematical programming methods. This paper presents it to the research of short-term scheduling of hydro plant. Accord to the application of the hydro unit, the results shows that reasonable and effective.


2014 ◽  
Vol 931-932 ◽  
pp. 1129-1133
Author(s):  
Natee Panagant ◽  
Sujin Bureerat

A differential evolution (DE) algorithm has been employed to approximate the solution of a nonlinear single pendulum equation. The solution has been approximated as a Fourier series expansion form. Then, weighted-residual and penalty functions are employed to transform the problem into a constrained optimization problem while optimum solutions will be carried out by DE. This paper also studies an effect of a scaling factor of DE to the results. The results reveal that the scaling factor significantly affects the convergent speed and accuracy of DE. Approximate solutions well agree with the exact solutions for the scaling factor being 0.5.


2021 ◽  
Author(s):  
Van-Tinh Nguyen ◽  
Ngoc-Tam Bui

This chapter addresses an approach to generate 3D gait for humanoid robots. The proposed method considers gait generation matter as optimization problem with constraints. Firstly, trigonometric function is used to produce trial gait data for conducting simulation. By collecting the result, we build an approximation model to predict final status of the robot in locomotion, and construct optimization problem with constraints. In next step, we apply an improve differential evolution algorithm with Gauss distribution for solving optimization problem and achieve better gait data for the robot. This approach is validated using Kondo robot in a simulated dynamic environment. The 3D gait of the robot is compared to human in walk.


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