A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Saad, Abdulbaset; Dong, Zuomin; Karimi, Mohammad

doi:10.3390/a10040120

Cited by 18 publications

(8 citation statements)

References 96 publications

(98 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enables the formation of a relationship between the variables z and s, as indicated in Equation (14). In this context, ∆t represents the time step, whereas s is the complex number s = σ + jω.…”

Section: Eigenvalue Analysis For Optimum Switch Parameter Selectionmentioning

confidence: 99%

Optimal Selection of Switch Model Parameters for ADC-Based Power Converters

Alsarayreh,

Sütő

2023

Energies

View full text Add to dashboard Cite

Real-time hardware-in-the-loop-(HIL) simulation integration is now a fundamental component of the power electronics control design cycle. This integration is required to test the efficacy of controller implementations. Even though hardware-in-the-loop-(HIL) tools use FPGA devices with computing power that is rapidly evolving, developers constantly need to balance the ease of deploying models with acceptable accuracy. This study introduces a methodology for implementing a full-bridge inverter and buck converter utilising the associate-discrete-circuit-(ADC) model, which is optimised for real-time simulator applications. Additionally, this work introduces a new approach for choosing ADC parameter values by using the artificial-bee-colony-(ABC) algorithm, the firefly algorithm (FFA), and the genetic algorithm (GA). The implementation of the ADC-based model enables the development of a consistent architecture in simulation, regardless of the states of the switches. The simulation results demonstrate the efficacy of the proposed methodology in selecting optimal parameters for an ADC-switch-based full-bridge inverter and buck converter. These results indicate a reduction in overshoot and settling time observed in both the output voltage and current of the chosen topologies.

show abstract

Section: Eigenvalue Analysis For Optimum Switch Parameter Selectionmentioning

confidence: 99%

Optimal Selection of Switch Model Parameters for ADC-Based Power Converters

Alsarayreh,

Sütő

2023

Energies

View full text Add to dashboard Cite

show abstract

“…Ideally, the computation time increases with the number of algorithmic variables. Saad et al (2017) Fig. 4 Box-plot for comparison between algorithms (left) and its definition (right)…”

Section: Computation Costmentioning

confidence: 99%

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

2020

View full text Add to dashboard Cite

The simulation-driven metaheuristic algorithms have been successful in solving numerous problems compared to their deterministic counterparts. Despite this advantage, the stochastic nature of such algorithms resulted in a spectrum of solutions by a certain number of trials that may lead to the uncertainty of quality solutions. Therefore, it is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives. The performance of a randomized metaheuristic algorithm can be divided into efficiency and effectiveness measures. The efficiency relates to the algorithm's speed of finding accurate solutions, convergence, and computation. On the other hand, effectiveness relates to the algorithm's capability of finding quality solutions. Both scopes are crucial for continuous and discrete problems either in single-or multi-objectives. Each problem type has different formulation and methods of measurement within the scope of efficiency and effectiveness performance. One of the most decisive verdicts for the effectiveness measure is the statistical analysis that depends on the data distribution and appropriate tool for correct judgments.

show abstract

“…This theory is based on the mechanism representing the survival of the fittest individuals over a population. Genetic algorithms have been widely applied to several disciplines, such as feature selection [39], biomedicine [40], mechanical systems [41], etc.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media

2020

View full text Add to dashboard Cite

One of the most challenging problems that are still open in the field of materials science is the 3D reconstruction of porous media using information from a single 2D thin image of the original material. Such a reconstruction is only feasible subject to some important assumptions that need to be made as far as the statistical properties of the material are concerned. In this study, the aforementioned problem is investigated as an explicitly formulated optimization problem, with the phase of each porous material point being decided such that the resulting 3D material model shows the same statistical properties as its corresponding 2D version. Based on this problem formulation, herein for the first time, several traditional (genetic algorithms—GAs, particle swarm optimization—PSO, differential evolution—DE), as well as recently proposed (firefly algorithm—FA, artificial bee colony—ABC, gravitational search algorithm—GSA) nature-inspired optimization algorithms were applied to solve the 3D reconstruction problem. These algorithms utilized a newly proposed data representation scheme that decreased the number of unknowns searched by the optimization process. The advantages of addressing the 3D reconstruction of porous media through the application of a parallel heuristic optimization algorithm were clearly defined, while appropriate experiments demonstrating the greater performance of the GA algorithm in almost all the cases by a factor between 5%–84% (porosity accuracy) and 3%–15% (auto-correlation function accuracy) over the PSO, DE, FA, ABC, and GSA algorithms were undertaken. Moreover, this study revealed that statistical functions of a high order need to be incorporated into the reconstruction procedure to increase the reconstruction accuracy.

show abstract

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Cited by 18 publications

References 96 publications

Optimal Selection of Switch Model Parameters for ADC-Based Power Converters

Optimal Selection of Switch Model Parameters for ADC-Based Power Converters

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media

Contact Info

Product

Resources

About