Geodesic
Some local and global search balancing methods in parallel global
Numerical methods and programming, Tome 11 (2010) no. 4, pp. 382-387.

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The paper continues the study of the informational-statistics approach for minimizing multiextremal functions with nonconvex constraints called the index method of global optimization. The procedure of solving multidimensional problems is reduced to solving equivalent one-dimensional ones. This reduction is based on using the Peano curves reflecting the unit segment of the real axis to a hypercube uniquely. The technique of constructing a set of Peano curves is used (rotated evolvements). It can be efficiently applied to solving a problem on a cluster with tens and hundreds processors. The main attention is paid to the use of a mixed local-global computational scheme to speed up the convergence of the parallel algorithm as well as to the application of a local descent after each improvement of a global optimum estimate (record local refinement) followed by the global search continuation.
Keywords: global optimization; black-box optimization; constrained optimization; index approach; rotated evolvements; mixed strategy; local-global strategy; local descent; GKLS; operating characteristics. 
@article{VMP_2010_11_4_a10,
     author = {K. A. Barkalov and V. V. Ryabov and S. V. Sidorov},
     title = {Some local and global search balancing methods in parallel global},
     journal = {Numerical methods and programming},
     pages = {382--387},
     publisher = {mathdoc},
     volume = {11},
     number = {4},
     year = {2010},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VMP_2010_11_4_a10/}
}
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K. A. Barkalov; V. V. Ryabov; S. V. Sidorov. Some local and global search balancing methods in parallel global. Numerical methods and programming, Tome 11 (2010) no. 4, pp. 382-387. http://geodesic.mathdoc.fr/item/VMP_2010_11_4_a10/