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A heuristic computing scheme for the numerical solutions of the freelance model
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DOI：10.1080/02286203.2026.2682956.png)
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Abstract
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This study presents numerical results for the freelancing model using a proposed stochastic scheme. The mathematical form of the freelancing model is divided into three categories: the working population, the freelancer population, and the dissemination of information among freelancers. The process of a stochastic computing feed-forward neural network is outlined, utilizing the log-sigmoid function as the activation function and incorporating 10 neurons. The error function is constructed using the differential freelancing model and then optimized through a hybrid search scheme that combines global genetic algorithms with a local active-set method. The efficiency of the solver is tested through the obtained and reference results based on the Runge–Kutta method, whereas the absolute error values around 10−05 to 10−07 improve the worth of the technique. Furthermore, the statistical performances are implemented to authenticate the reliability of the proposed scheme for the numerical solutions of the freelancing model.
Keywords:
Freelancing model
genetic algorithm
activation function
active set
optimization
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