A Direct Search Algorithm for Global Optimization
AbstractA direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization. View Full-Text
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Baeyens, E.; Herreros, A.; Perán, J.R. A Direct Search Algorithm for Global Optimization. Algorithms 2016, 9, 40.
Baeyens E, Herreros A, Perán JR. A Direct Search Algorithm for Global Optimization. Algorithms. 2016; 9(2):40.Chicago/Turabian Style
Baeyens, Enrique; Herreros, Alberto; Perán, José R. 2016. "A Direct Search Algorithm for Global Optimization." Algorithms 9, no. 2: 40.
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