乐胖代购免代理版

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Our approach, which we call TensorCRO, takes advantage of the TensorFlow framework to represent CRO\u2010SL as a series of tensor operations, allowing it to run on GPU and search for solutions in a faster and more efficient way. We evaluate the performance of the proposed implementation across a wide range of benchmark functions commonly used in optimization research (such as the Rastrigin, Rosenbrock, Ackley, and Griewank functions), and we show that GPU execution leads to considerable speedups when compared to its CPU counterpart. Then, when comparing TensorCRO to other state\u2010of\u2010the\u2010art optimization algorithms (such as the Genetic Algorithm, Simulated Annealing, and Particle Swarm Optimization), the results show that TensorCRO can achieve better convergence rates and solutions than other algorithms within a fixed execution time, given that the fitness functions are also implemented on TensorFlow. Furthermore, we also evaluate the proposed approach in a real\u2010world problem of optimizing power production in wind farms by selecting the locations of turbines; in every evaluated scenario, TensorCRO outperformed the other meta\u2010heuristics and achieved solutions close to the best known in the literature. Overall, our implementation of the CRO\u2010SL algorithm in TensorFlow GPU provides a new, fast, and efficient approach to solving optimization problems, and we believe that the proposed implementation has significant potential to be applied in various domains, such as engineering, finance, and machine learning, where optimization is often used to solve complex problems. Furthermore, we propose that this implementation can be used to optimize models that cannot propagate an error gradient, which is an excellent choice for non\u2010gradient\u2010based optimizers.<\/jats:p>","DOI":"10.1111\/exsy.13713","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T04:45:53Z","timestamp":1725597953000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TensorCRO<\/scp>: A TensorFlow<\/scp>\u2010based implementation of a multi\u2010method ensemble for optimization"],"prefix":"10.1111","volume":"41","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1061-0972","authenticated-orcid":false,"given":"A.","family":"Palomo\u2010Alonso","sequence":"first","affiliation":[{"name":"Department of Signal Processing and Communications Universidad de Alcal\u00e1 Madrid Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4702-6374","authenticated-orcid":false,"given":"V. 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M.","family":"Moreno\u2010Saavedra","sequence":"additional","affiliation":[{"name":"Department of Signal Processing and Communications Universidad de Alcal\u00e1 Madrid Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4480-6132","authenticated-orcid":false,"given":"E.","family":"Lorente\u2010Ramos","sequence":"additional","affiliation":[{"name":"Department of Signal Processing and Communications Universidad de Alcal\u00e1 Madrid Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4456-9886","authenticated-orcid":false,"given":"J.","family":"P\u00e9rez\u2010Aracil","sequence":"additional","affiliation":[{"name":"Department of Signal Processing and Communications Universidad de Alcal\u00e1 Madrid Spain"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9312-4023","authenticated-orcid":false,"given":"C. 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