This paper presents a covering algorithm for solving bound-constrained global minimization problems with a differentiable cost function. In the proposed algorithm, we suggest to explore the feasible domain using a one-dimensional global search algorithm through a number of parametric curves that are relatively spread and simultaneously scan the search space. To accelerate the corresponding algorithm, we incorporate a multivariate quasi-Newton local search algorithm to spot the lowest regions. The proposed algorithm converges in a finite number of iterations to an -approximation of the global minimum. The performance of the algorithm is demonstrated through numerical experiments on some typical test functions.
Ziadi, R. and Bencherif-Madani, A. (2023). A mixed algorithm for smooth global optimization. Journal of Mathematical Modeling, 11(2), 207-228. doi: 10.22124/jmm.2022.23133.2061
MLA
Ziadi, R. , and Bencherif-Madani, A. . "A mixed algorithm for smooth global optimization", Journal of Mathematical Modeling, 11, 2, 2023, 207-228. doi: 10.22124/jmm.2022.23133.2061
HARVARD
Ziadi, R., Bencherif-Madani, A. (2023). 'A mixed algorithm for smooth global optimization', Journal of Mathematical Modeling, 11(2), pp. 207-228. doi: 10.22124/jmm.2022.23133.2061
CHICAGO
R. Ziadi and A. Bencherif-Madani, "A mixed algorithm for smooth global optimization," Journal of Mathematical Modeling, 11 2 (2023): 207-228, doi: 10.22124/jmm.2022.23133.2061
VANCOUVER
Ziadi, R., Bencherif-Madani, A. A mixed algorithm for smooth global optimization. Journal of Mathematical Modeling, 2023; 11(2): 207-228. doi: 10.22124/jmm.2022.23133.2061