scholarly journals Applying Improve Differential Evolution Algorithm for Solving Gait Generation Problem of Humanoid Robots

2021 ◽  
Author(s):  
Van-Tinh Nguyen ◽  
Ngoc-Tam Bui
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Dynamic Environment ◽  
Humanoid Robots ◽  
Gait Generation ◽  
Construct Optimization ◽  
Evolution Algorithm ◽  
Gauss Distribution ◽  
Generation Problem

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.

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
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Approximate Solutions ◽  
Constrained Optimization Problem ◽  
Stiff System ◽  
Order Ordinary Differential Equation ◽  
First Order ◽  
Evolutionary Optimization Algorithms ◽  
Evolution Algorithm

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.

Niche Differential Evolution Algorithm and its Application in Multimodal Function Optimization

2011 ◽  
Vol 308-310 ◽  
pp. 2431-2435 ◽  
Author(s):  
Na Li ◽  
Yuan Xiang Li ◽  
Zhi Guo Huang ◽  
Yong Wang
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Function Optimization ◽  
Multimodal Optimization ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
Optimal Value ◽  
Evolution Algorithm

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.

The Differential Evolution and its Application in Short-Term Scheduling of Hydro Unit

2011 ◽  
Vol 243-249 ◽  
pp. 4642-4646
Author(s):  
Hai Ying Deng ◽  
Zhi Gang Zhang ◽  
Yi Gang Yu
Keyword(s):  
Mathematical Programming ◽  
Differential Evolution ◽  
Optimization Problem ◽  
Search Strategy ◽  
Differential Evolution Algorithm ◽  
Short Term ◽  
Specific Memory ◽  
Complex Optimization ◽  
Evolution Algorithm ◽  
Optimization Search

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.

scholarly journals A novel differential evolution algorithm for economic power dispatch problem

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Pooja
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Optimization Problem ◽  
Linear Optimization ◽  
Differential Evolution Algorithm ◽  
Economic Power ◽  
Power Dispatch ◽  
Evolution Algorithm ◽  
Economic Power Dispatch

<p style='text-indent:20px;'>In power systems, Economic Power dispatch Problem (EPP) is an influential optimization problem which is a highly non-convex and non-linear optimization problem. In the current study, a novel version of Differential Evolution (NDE) is used to solve this particular problem. NDE algorithm enhances local and global search capability along with efficient utilization of time and space by making use of two elite features: selfadaptive control parameter and single population structure. The combined effect of these concepts improves the performance of Differential Evolution (DE) without compromising on quality of the solution and balances the exploitation and exploration capabilities of DE. The efficiency of NDE is validated by evaluating on three benchmark cases of the power system problem having constraints such as power balance and power generation along with nonsmooth cost function and is compared with other optimization algorithms. The Numerical outcomes uncovered that NDE performed well for all the benchmark cases and maintained a trade-off between convergence rate and efficiency.</p>

scholarly journals Adaptive Constrained Differential Evolution Algorithm by Using Generalized Opposition-Based Learning

2019 ◽  
Vol 37 (5) ◽  
pp. 1000-1010
Author(s):  
Wenhai Wu ◽  
Xiaofeng Guo ◽  
Siyu Zhou ◽  
Jintao Liu
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Simple Structure ◽  
Differential Evolution Algorithm ◽  
Initial Population ◽  
Mutation Operator ◽  
Competition Mechanism ◽  
Opposition Based Learning ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

Differential evolution is a global optimization algorithm based on greedy competition mechanism, which has the advantages of simple structure, less control parameters, higher reliability and convergence. Combining with the constraint-handling techniques, the constraint optimization problem can be efficiently solved. An adaptive differential evolution algorithm is proposed by using generalized opposition-based learning (GOBL-ACDE), in which the generalized opposition-based learning is used to generate initial population and executes the generation jumping. And the adaptive trade-off model is utilized to handle the constraints as the improved adaptive ranking mutation operator is adopted to generate new population. The experimental results show that the algorithm has better performance in accuracy and convergence speed comparing with CDE, DDE, A-DDE and. And the effect of the generalized opposition-based learning and improved adaptive ranking mutation operator of the GOBL-ACDE have been analyzed and evaluated as well.

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