A study of the Classical Differential Evolution control parameters

Peñuñuri, F.; Cab, C.; Carvente, O.; Zambrano-Arjona, M. A.; Tapia, J.

doi:10.1016/j.swevo.2015.08.003

Cited by 42 publications

(18 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been shown that the control parameters and learning strategies are highly problemdependent. 40 It is time-consuming to tune parameters manually for different problems. SaDE 41 and SaNSDE (self-adaptive differential evolution with neighborhood search) 42 use multiple strategies adaptively based on the success during previous generations with adaptive values of F and CR using different schemes.…”

Section: Population Representation and Initializationmentioning

confidence: 99%

Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization

Sun

Zhang

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Decomposing the large-scale problem into small-scale subproblems and optimizing them cooperatively are critical steps for solving large-scale optimization problem. This article proposes a cooperative differential evolution with utility-based adaptive grouping. The problem decomposition is adaptively executed by the two mechanisms of circular sliding controller and relation matrix, which consider the variable interactions on the basis of the short-term and long-term utilities, respectively. The circular sliding controller provides baselines for the subproblem optimizer. The size of the sliding window and the sliding speed in the controller are adjusted adaptively so that the variables with higher activeness can be optimized extensively. The relation matrix-based grouping strategy enables interacted variables to be grouped into the same subproblem with higher probabilities. The novelty is that decomposition is conducted as the optimization process without extra computational burden. For subproblem optimization, we use a self-adaptive differential evolution operator that adaptively adjusts the parameters to guide the search to the optimum solutions of the subproblems. Experiments on the benchmarks of CEC2008 and CEC2010, and practical problems show the effectiveness of the proposed algorithm.

show abstract

Section: Population Representation and Initializationmentioning

confidence: 99%

Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization

Sun

Zhang

et al. 2019

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Even though there are several wake effect models, one of the simplest is the one proposed by [27]; in that model some assumptions are considered, such as same height hubs, entrainment constant, turbine radii, among others. Also, that model is easy to program [48], which makes it one of the most preferred. A simple representation of the wake effect model is given in Figure 1, where the first turbine's wake is not affecting the second turbine.…”

Section: Wake Effect Modelingmentioning

confidence: 99%

“…In the consulted literature (e.g. [48], [9], [19]), in order to estimate the energy produced by the wind farm, three cases were considered: Case 1: Uniform wind speed and direction: k = 1, u 0 = 8m/s. Case 2: Uniform wind speed and variable direction:…”

Section: Fig 2 Partial Wake Areamentioning

confidence: 99%

“…With respect to λ 1 , this is a factor empirically selected to penalize the value of the objective function g every time that any other turbine is closer than a security distance to the other turbines in the wind farm (usually, a standard value for such distance is ten times the turbine radius [48]). [45], and others.…”

Section: Fig 2 Partial Wake Areamentioning

confidence: 99%

“…In that Computación y Sistemas, Vol. 22 sense, a restriction to optimize the WFLP is the turbines location, due to the wake effect [48,9]. Several models for the wake effect have been proposed, with the Jensen model being the most common [27], due to its mathematical simplicity, it is easily programmed, and also because it represents relatively well the wake decay effect, when it is compared against more complicated models [30].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Offshore Wind Farm Layout Optimization via Differential Evolution

Enciso

Haro

Oliva

et al. 2018

CyS

View full text Add to dashboard Cite

The Wind Farm Layout Problem (WFLP) consists in the placement of eolic generators (either in a grid, or at any position) into a delimited terrain. Several factors are taken into account to solve the WFLP, which include produced energy, costs-environmental, installation, maintenance, etc-, average useful life of turbines, among other. Likewise, optimization techniques involve the use of one or more objective functions, considering traditional as well as evolutionary approaches. Differential Evolution (DE) is an algorithm proposed for global optimization, whose operators are both simple to program and to utilize, still providing good convergence properties. The original authors of DE suggested its first five variants, which are: best/1/bin, best/2/bin, current − to − best/1/bin, rand/1/bin, and rand/2/bin. In this article it is proposed the comparison of five DE variants when they are used to solve 25 different instances of the WFLP; experimental results show that DE/best/1/bin outperforms the remaining algorithms in terms of convergence velocity as well as in the quality of the obtained wind-farm.

show abstract

Synchronizing lockdown and vaccination policies for COVID‐19: An optimal control approach based on piecewise constant strategies

Salcedo-Varela

Peñuñuri

González‐Sánchez

et al. 2023

Optim Control Appl Methods

View full text Add to dashboard Cite

We are still facing the devastating consequences of COVID‐19. At the beginning of this pandemic, lockdown and non‐pharmaceutical measures were the unique, effective strategy to overcome the ongoing outbreak. After almost a year, an exceptional effort gave the first efficient protective vaccines. Despite these significant advances, new challenges as its mass production and fair distribution emerge. Our work aims to address this balance by formulating an optimal epidemic control problem controlled by lockdown and vaccination but in a synchronized manner. In such a way that the sought‐after solution optimizes the burden and economic implications of COVID‐19 infections and deaths. Thus, we formulate an optimal control problem with a differential equation to describe the spread of COVID‐19. Our formulation measures the efficiency of these controls by a functional cost involving the burden of COVID‐19 quantified in DALYs and the costs regarding vaccination and lockdown. Then we minimize this cost subject to the controlled system and find optimal policies that are constant in time intervals of a given size. To this end, we apply the well‐established heuristic method known as differential evolution. One of the advantages of these policies relies on their practical implementation since the health authority has to make only a finite number of different decisions. Our methodology to find optimal policies allows changes in the dynamics, the cost functional, or how frequently the policymaker changes actions. We show how a well‐synchronized tradeoff between vaccination and lockdown could under‐peak of the outbreak, with a delicate balance to overcome possible economic consequences.

show abstract

A study of the Classical Differential Evolution control parameters

Cited by 42 publications

References 19 publications

Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization

Cooperative differential evolution framework with utility-based adaptive grouping for large-scale optimization

Offshore Wind Farm Layout Optimization via Differential Evolution

Synchronizing lockdown and vaccination policies for COVID‐19: An optimal control approach based on piecewise constant strategies

Contact Info

Product

Resources

About