Bare bones differential evolution

Omran, Mahamed G. H.; Engelbrecht, Andries P.; Salman, Ayed A.

doi:10.1016/j.ejor.2008.02.035

Cited by 181 publications

(71 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Qin et al [20] proposed Self-adaptive Differential Evolution Algorithm (SaDE) which can adjust CR, and choose strategy automatically during optimization. SaDE outperforms conventional DE variants and the other adaptive DE, such as SDE [21] and jDE [22], in terms of higher successful rate.…”

Section: Introductionmentioning

confidence: 99%

“…It is time consuming to search the most appropriate strategy and parameters by trial and error. Hence, Omran et al [21] and Brest et al [22] have proposed different methods to adjust CR and F. However, appropriate mechanism for choosing suitable strategies is not considered in [21,22]. Qin et al [20] proposed Self-adaptive Differential Evolution Algorithm (SaDE) which can adjust CR, and choose strategy automatically during optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Tušar and Filipič [17] carried out comparisons between DE-based variants DEMO and basic GA on multiobjective optimization problem and their result showed that DEMO outperforms basic GA. Vesterstrøm and Thomsen [18] noted that DE outperforms PSO and Evolutionary Algorithms (EAs) on majority of numerical benchmark problems. DE consists of population size (NP), scaling factor ( ), and crossover rate (CR) which significantly affect the performance of DE [19][20][21][22]. Different problems require different parameters and strategies for effective optimization.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved ZMP-Based CPG Model of Bipedal Robot Walking Searched by SaDE

Fung

Jing

2014

ISRN Robotics

View full text Add to dashboard Cite

This paper proposed a method to improve the walking behavior of bipedal robot with adjustable step length. Objectives of this paper are threefold. (1) Genetic Algorithm Optimized Fourier Series Formulation (GAOFSF) is modified to improve its performance.(2) Self-adaptive Differential Evolutionary Algorithm (SaDE) is applied to search feasible walking gait. (3) An efficient method is proposed for adjusting step length based on the modified central pattern generator (CPG) model. The GAOFSF is modified to ensure that trajectories generated are continuous in angular position, velocity, and acceleration. After formulation of the modified CPG model, SaDE is chosen to optimize walking gait (CPG model) due to its superior performance. Through simulation results, dynamic balance of the robot with modified CPG model is better than the original one. In this paper, four adjustable factors ( hs,support , hs,swing , ks,support , and ks,swing ) are added to the joint trajectories. Through adjusting these four factors, joint trajectories are changed and hence the step length achieved by the robot. Finally, the relationship between (1) the desired step length and (2) an appropriate set of hs,support , hs,swing , ks,support , and ks,swing searched by SaDE is learnt by Fuzzy Inference System (FIS). Desired joint angles can be found without the aid of inverse kinematic model.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved ZMP-Based CPG Model of Bipedal Robot Walking Searched by SaDE

Fung

Jing

2014

ISRN Robotics

View full text Add to dashboard Cite

show abstract

“…Omran et al 38 proposed a hybrid version of Bare Bones PSO and DE called it BBDE. In their approach, they combined the concept of barebones PSO with self adaptive DE strategies.…”

Section: A Brief Literature Review On Earlier Work Donementioning

confidence: 99%

Swarm Directions Embedded Differential Evolution for Faster Convergence of Global Optimization Problems

Ali

Pant

Abraham

et al. 2012

Int. J. Artif. Intell. Tools

View full text Add to dashboard Cite

In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE algorithm, information sharing mechanism of PSO is embedded in the contracted search space obtained by the basic DE algorithm. This is done to maintain a balance between the two antagonist factors; exploration and exploitation thereby obtaining a faster convergence. The embedding of swarm directions to the basic DE algorithm is done with the help of a "switchover constant " called α which keeps a record of the contraction of search space. The proposed HDE algorithm is tested on a set of 10 unconstrained benchmark problems and four constrained real life, mechanical design problems. Empirical studies show that the proposed scheme helps in improving the convergence rate of the basic DE algorithm without compromising with the quality of solution.

show abstract

“…Three different DE-based approaches to self-adaptation were investigated [23][9] [22], and it was found that the the approach of Brest et al [9] is the most effective of the three when used in conjunction with CDE.…”

Section: The Self-adaptive Dynpopde Algorithmmentioning

confidence: 99%