Application of metaheuristic algorithms in nano-process parameter optimization

Norlina, M. S.; Mazidah, P.; Sin, N. D. Md; Rusop, M.

doi:10.1109/cec.2015.7257212

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…For optimization algorithms comparisons were scarce, only 4 articles compared metaheuristic algorithms. GA outperformed PSO in optimizing the concentration, sonication time, pH and amount of adsorbent for the removal of ethyl violet in wastewater [108], and outperformed GSA and PSO in optimizing parameter combination among selected datasets for ZnO synthesis [112]. In other words, PSO outperformed GA in optimizing the solid concentration and temperature for the removal of crystal violet using bimetallic Fe/Ni NPs [113], and in training an ANFIS network to predict the thermophysical properties of Al2O3-MWCNT/oil hybrid nanofluid [114].…”

Section: Comparisonmentioning

confidence: 99%

Modelling and optimization of nanovector synthesis for applications in drug delivery systems

Villaseñor-Cavazos¹,

Torres-Valladares²,

Lozano³

2021

Preprint

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Nanovectors (NVs), based on nanostructured matter such as nanoparticles (NPs), have proven to perform as excellent drug delivery systems. However, due to the great variety of potential NVs, including NPs materials and their functionalization, in addition to the plethora of molecules that could transport, this fields presents a great challenge in terms of resources to find NVs with the most optimal physicochemical properties such as particle size and drug loading, where most of efforts rely on trial and error experimentation. In this regard, Artificial intelligence (AI) and metaheuristic algorithms offer efficient of the state-of-the-art modelling and optimization, respectively. This review focuses, through a systematic search, on the use of artificial intelligence and metaheuristic algorithms for nanoparticle synthesis in drug delivery systems. The main findings are: neural networks are better at modelling NVs properties than linear regression algorithms and response surface methodology, there is a very limited number of studies comparing AI or metaheuristic algorithm, and there is no information regarding the appropriateness of calculations of the sample size. Based on these findings, multilayer perceptron artificial neural network and adaptive neuro fuzzy inference system were tested for their modelling performance with a NV dataset; finding the latter the better algorithm. For metaheuristic algorithms, benchmark functions were optimized with cuckoo search, firefly algorithm, genetic algorithm and symbiotic organism search; finding cuckoo search and symbiotic organism search with the best performance. Finally, methods to estimate appropriate sample size for AI algorithms are discussed.

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Section: Comparisonmentioning

confidence: 99%

Modelling and optimization of nanovector synthesis for applications in drug delivery systems

Villaseñor-Cavazos¹,

Torres-Valladares²,

Lozano³

2021

Preprint

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“…Unlike GA and PSO, which are based on biological phenomena, GSA is an optimization method based on the laws of gravity and mass interaction in physics. GSA has been verified to be more efficient than other optimization methods in optimizing the controller parameters [31], but still suffers from localized and premature convergence problems. However, almost all of the above research works have used the conventional PID controller which ultimately leads to a sub-optimal response.…”

Section: Introductionmentioning

confidence: 99%

Fractional-PID and Its Parameter Optimization for Pumped Storage Units Considering the Complicated Conduit System

Zhou,

Zheng,

et al. 2023

Water

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Speed governing control is significant in ensuring the stable operation of pumped storage units. In this study, a state-space equation mathematical model of the pumped storage governing system considering the complex hydraulic pipeline structure of the pumped storage plant is proposed to describe the system’s dynamic behaviors under small disturbance conditions. Considering the frequent operating condition transitions and the complicated nonlinear dynamic characteristics of the pumped storage units, the fractional-order PID (FOPID) scheme that possesses a higher degree of control freedom than the traditional PID scheme is discussed in detail. To optimize the control parameters of the unit governor, an improved gravitational search algorithm (IGSA) that combines the basic searching mechanisms of the gravitational search algorithm and chaotic search, elastic sphere boundary treatment, and elite guidance strategy is developed. Comparative studies have been carried out under frequency and load disturbance conditions. Simulation results indicate that the control performance of FOPID is better than that of PID under diverse operating conditions and the proposed IGSA has satisfactory parameter optimization capability.

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