A modified particle swarm optimization algorithm for parameter estimation of a biological system

Mosayebi, Raziyeh; Bahrami, Fariba

doi:10.1186/s12976-018-0089-6

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…Evolutionary algorithms fundamentally consist of a series of generations of parameter sets that improve over time (Ba€ck, 1996). They mainly include genetic algorithms (Michalakelis et al, 2012), differential evolution (Storn and Price, 1997), ant colonies (Dorigo and Stu€tzle, 2004), and particle swarm optimization (Mosayebi and Bahrami, 2018), but also others.…”

Section: Evolutionary Search Methodsmentioning

confidence: 99%

Methods of quantifying interactions among populations using Lotka-Volterra models

et al. 2022

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The Lotka-Volterra (LV) model was introduced in the early 20th Century to describe predator-prey systems. Since then, the model has been expanded to capture the dynamics of numerous types of interacting populations and to include the effects of external factors from the environment. Despite many simplifying assumptions, the LV approach has proven to be a very valuable tool for gaining insights into the dynamics of diverse biological interaction systems. In particular, recognizing the critical importance of microbiomes for human and environmental heath, LV systems have become effective tools of analysis and, indeed, the default for quantitatively assessing interactions within these large microbial communities. Here we present an overview of parameter inference methods for LV systems, specifically addressing individuals entering the field of biomathematical modeling, who have a modest background in linear algebra and calculus. The methods include traditional local and global strategies, as well as a recently developed inference method based strictly on linear algebra. We compare the different strategies using both lab-acquired and synthetic time series data. We also address a recent debate within the scientific community of whether it is legitimate to compose large models from information inferred for the dynamics of subpopulations. In addition to parameter estimation methods, the overview includes preparatory aspects of the inference process, including data cleaning, smoothing, and the choice of an adequate loss function. Our comparisons demonstrate that traditional fitting strategies, such as gradient descent optimization and differential evolution, tend to yield low residuals but sometimes overfit noisy data and incur high computation costs. The linear-algebra-based method produces a satisfactory solution much faster, generally without overfitting, but requires the user to estimate slopes from the time series, which can introduce undue error. The results also suggest that composing large models from information regarding sub-models can be problematic. Overall, there is no clear “always-best method” for inferring parameters from data, and prudent combinations may be the best strategy.

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Section: Evolutionary Search Methodsmentioning

confidence: 99%

Methods of quantifying interactions among populations using Lotka-Volterra models

et al. 2022

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“…Banks are using big data as well as data science to boost profits by getting new insights from existing data and improving predictions based on that data [4]. To provide forecasts for a variety of expert systems, including liquidity, risk, customer attrition, fraud detection, and revenue, as well as making educated decisions, as a result, AI-and ML-based technologies may be of great assistance to them in determining their credit score [5].…”

Section: Introductionmentioning

confidence: 99%

Digital Universal Financial Credit Risk Analysis Using Particle Swarm Optimization Algorithm with Structure Decision Tree Learning-Based Evaluation Model

Tian

2022

Wireless Communications and Mobile Computing

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Predictions of credit risk, model reliability, monitoring, and efficient loan processing are all important factors in decision-making and transparency. Machine learning method is providing these people new hope. However, it is up to banking or nonbanking institutions to determine how they will implement this advanced method in order to decrease human biases in loan decision-making. Objective. This paper proposed the novel machine learning-based credit risk analysis in the digital banking evaluation model. The purpose of this research is to compare various ML algorithms in order to develop an accurate model for credit risk assessment utilising data from a genuine credit registry dataset. Aim is to design the classification-based model using particle swarm optimization (PSO) algorithm with structure decision tree learning (SDTL) in predicting credit risk. This system has the potential to improve quality criteria such as dependability, robustness, extensibility, and scalability. Features have been extracted and classified by the proposed PSO_SL model. Experimental Results. The data have been collected based on real time for credit analysis. Simulation is carried out in Python and optimal results are obtained in comparative analysis with existing techniques. The accuracy obtained by proposed technique is enhanced and the error rate of the design is minimized.

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“…As it is detailed later on, the assignment of suitable parametric probability distributions for the random initial conditions is performed via the Principle of Maximum Entropy (PME) [29]. These distributional parameters, together with the other model parameters, will be calculated using a tailor-made procedure based on the application of an optimization algorithm named Particle Swarm Optimization (PSO) [30][31][32][33]. Afterwards, we calculate predictions of the expectation of the aforementioned biological model at different time instants.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Random Microbial Growth in a Competitive Environment via Probability Density Functions

et al. 2021

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The Baranyi–Roberts model describes the dynamics of the volumetric densities of two interacting cell populations. We randomize this model by considering that the initial conditions are random variables whose distributions are determined by using sample data and the principle of maximum entropy. Subsequenly, we obtain the Liouville–Gibbs partial differential equation for the probability density function of the two-dimensional solution stochastic process. Because the exact solution of this equation is unaffordable, we use a finite volume scheme to numerically approximate the aforementioned probability density function. From this key information, we design an optimization procedure in order to determine the best growth rates of the Baranyi–Roberts model, so that the expectation of the numerical solution is as close as possible to the sample data. The results evidence good fitting that allows for performing reliable predictions.

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A modified particle swarm optimization algorithm for parameter estimation of a biological system

Cited by 9 publications

References 20 publications

Methods of quantifying interactions among populations using Lotka-Volterra models

Methods of quantifying interactions among populations using Lotka-Volterra models

Digital Universal Financial Credit Risk Analysis Using Particle Swarm Optimization Algorithm with Structure Decision Tree Learning-Based Evaluation Model

Uncertainty Quantification of Random Microbial Growth in a Competitive Environment via Probability Density Functions

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