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• W3133636088 abstract "Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths. PSO frequently accelerates optimization in practical applications, where gradients are not available and function evaluations expensive. Yet the traditional PSO algorithm ignores the potential knowledge that could have been gained of the objective function from the observations by individual particles. Hence, we draw upon concepts from Bayesian optimization and introduce a stochastic surrogate model of the objective function. That is, we fit a Gaussian process to past evaluations of the objective function, forecast its shape and then adapt the particle movements based on it. Our computational experiments demonstrate that baseline implementations of PSO (i. e., SPSO2011) are outperformed. Furthermore, compared to, state-of-art surrogate-assisted evolutionary algorithms, we achieve substantial performance improvements on several popular benchmark functions. Overall, we find that our algorithm attains desirable properties for exploratory and exploitative behavior." @default.
• W3133636088 created "2021-03-15" @default.
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• W3133636088 date "2021-11-01" @default.
• W3133636088 modified "2023-10-13" @default.
• W3133636088 title "Directed particle swarm optimization with Gaussian-process-based function forecasting" @default.
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• W3133636088 doi "https://doi.org/10.1016/j.ejor.2021.02.053" @default.
• W3133636088 hasPublicationYear "2021" @default.
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