Optimizing functionals using Differential Evolution

B., Cantún-Avila, K.; González‐Sánchez, David; Díaz‐Infante, Saúl; Peñuñuri, F.

doi:10.1016/j.engappai.2020.104086

Cited by 12 publications

(15 citation statements)

References 20 publications

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“…The development of the metaheuristics begin with the Evolutionary Algorithms (EAs), namely the Differential Evolution (DE) [ 102 ], Genetic Algorithm (GA), and Genetic Programming (GP). Different cultures have evolved and learned how to adapt the changes based on Darwin's evolution theory.…”

Section: Reservoir Operation Optimisation Innovation and Techniquesmentioning

confidence: 99%

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Lai

Huang

Koo

et al. 2022

Arch Computat Methods Eng

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Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.

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Section: Reservoir Operation Optimisation Innovation and Techniquesmentioning

confidence: 99%

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Lai

Huang

Koo

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Some authors commented the quality of solutions obtained, but these studies also rather lack a detailed comparison. Unfortunately, this is frequent in epidemiological papers, even not related to the current pandemic; for example, Cantun-Avila et al ( 2021 ) proposed to use DE for calibration of SEIR model for the epidemic of 2003 SARS virus, but the results were not compared against other methods. With respect to COVID-19 disease, Ames et al ( 2020 ) used DE, CMA-ES and NSGA-II algorithms to calibrate 3-dimensioanl SIR and 5-dimensional SIRHD models; it was unclear why multi-objective NSGA-II was used together with single-objective DE and CMA-ES.…”

Section: Applications Of Differential Evolution and Particle Swarm Optimization Against Covid-19mentioning

confidence: 99%

Differential evolution and particle swarm optimization against COVID-19

Piotrowski

Piotrowska

2021

Artif Intell Rev

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COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

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“…The method is designed to optimize functions f : ℝ n → ℝ. Nevertheless, DE can be applied to optimize a functional as stated in [24]. The method can be coded following Algorithm 1, where an initial random population on the search space 𝒱 of size N p is subjected to mutation, crossover and selection.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…In the optimization of this study, the mutation scale factor F and the crossover probability C r were taken as 1 and 0.3 respectively, additional N p has been taken as 4 times the number of parameters (the dimension of the vector used to describe the two controls—see [24]), which in our case was of 180. As stopping criteria we have used a maximum number of generations which is taken as 5000.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Optimal piecewise constant vaccination and lockdown policies for COVID-19

Salcedo-Varela¹,

Peñuñuri

González‐Sánchez

et al. 2021

Preprint

Self Cite

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We formulate a controlled system of ordinary differential equations, with vaccination and lockdown interventions as controls, to simulate the mitigation of COVID-19. The performance of the controls is measured through a cost functional involving vaccination and lockdown costs as well as the burden of COVID19 quantified in DALYs. We calibrate parameters with data from Mexico City and Valle de Mexico. By using differential evolution, we minimize the cost functional subject to the controlled system and find optimal policies that are constant in time intervals of a given size. The main advantage of these policies relies on its practical implementation since the health authority has to make only a finite number of different decisions. Our methodology to find optimal policies is relatively general, allowing changes in the dynamics, the cost functional, or the frequency the policymaker changes actions.

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Optimizing functionals using Differential Evolution

Cited by 12 publications

References 20 publications

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Differential evolution and particle swarm optimization against COVID-19

Optimal piecewise constant vaccination and lockdown policies for COVID-19

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