Differential evolution: A recent review based on state-of-the-art works

Ahmad, Muhammad Shakil; Isa, Nor Ashidi Mat; Lim, Wei Hong; Ang, Koon Meng

doi:10.1016/j.aej.2021.09.013

Cited by 195 publications

(89 citation statements)

References 169 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Fig. 3, a research framework is presented in four main steps: research definition, paper search, paper verification and research analysis [17].…”

Section: Methodsmentioning

confidence: 99%

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Qin

Isa

2023

IEEE Access

View full text Add to dashboard Cite

“…As shown in Fig. 3, a research framework is presented in four main steps: research definition, paper search, paper verification and research analysis [17].…”

Section: Methodsmentioning

confidence: 99%

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Qin

Isa

2023

IEEE Access

View full text Add to dashboard Cite

“…Therefore, in order to enhance the generalization ability of extreme learning machine under the influence of uncertain factors, this paper uses intelligent The optimization algorithm is combined with extreme learning machine. Compared with other evolutionary algorithms, the differential evolution algorithm has the advantages of simple execution, fast convergence speed and good global search performance [22], and is especially suitable for neural network optimization. The specific implementation steps of differential evolution (DE) can be found in [23].…”

Section: Differential Evolution Extreme Learning Machinementioning

confidence: 99%

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

Ali¹,

Galal²,

Alkhalaf³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids and energy management on the load side. Microgrid can effectively solve this problem by using its regulation and flexibility, and is considered to be an ideal platform. The traditional method of computing total transfer capability is difficult due to the central integration of wind farms. As a result, the differential evolution extreme learning machine is offered as a data mining approach for extracting operating rules for the total transfer capability of tie-lines in wind-integrated power systems. K-medoids clustering under the two-dimensional "wind powerload consumption" feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, a knowledge base for total transfer capability operating rule mining is created. Then, a novel method is used to filter redundant characteristics and find features that are closely associated to the total transfer capability in order to decrease the ultra-high dimensionality of operational features. Finally, by feeding the training data into the proposed algorithm, the total transfer capability operation rules are derived from the knowledge base. It can be seen that, the proposed algorithm can optimize the system performance with good accuracy and generality, according to numerical data.

show abstract

“…Although heuristic techniques cannot always guarantee the optimal result, they aim to produce satisfactory solutions, or solutions that are at least close to the optimal outcome, at a reasonable processing cost. MSAs use a variety of strategies to identify effective solutions to optimize problems starting from a large population of acceptable candidates, while making few assumptions about the problem being optimized [25]. Furthermore, the algorithms are bio-inspired since they have underlying mechanisms based on biological processes.…”

Section: Employed Algorithmsmentioning

confidence: 99%

“…One of the most famous EA is the Differential Evolution strategy [25,26], which is also one of the tested algorithm in this work. The main concept of this algorithm follows the genetic principles.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

A Model-Based Prognostic Framework for Electromechanical Actuators based on Metaheuristic Algorithms

Baldo¹,

Querques²,

Vedova³

et al. 2023

Preprint

View full text Add to dashboard Cite

The deployment of Electro-Mechanical Actuators plays an important role towards the adoption of the More Electric Aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs in place of more traditional hydraulic solutions is still set back due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA Prognostics and Health Management (PHM) system, capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The authors have developed a model-based prognostic framework for PMSM based EMAs leveraging a metaheuristic algorithm: Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity and proportional gain) are simulated thanks to a Reference Model, acting as a Numerical Test Bench, then detected and identified thanks to the envisioned prognostic method, which leverages a low fidelity Monitoring Model. The employed algorithms showed good results and prove that this strategy could be executed in pre-flight checks or during the flight at specific time intervals, with positive impacts on system safety and availability.

show abstract

Differential evolution: A recent review based on state-of-the-art works

Cited by 195 publications

References 169 publications

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

A Model-Based Prognostic Framework for Electromechanical Actuators based on Metaheuristic Algorithms

Contact Info

Product

Resources

About