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- W2071808953 abstract "This chapter suggests a parallel controlled random search method. The resulting algorithms generate more than one new point in parallel at each iteration. It is found that the parallel algorithms scale well against the number of processors. The parallel controlled random search algorithm is also compared with the genetic algorithm. In general, the formers are shown to be more efficient than a floating point coded genetic algorithm on the set of test problems. The result of initial application to design optimization looks promising. Unlike the standard CRS algorithms, the parallel algorithms (PCRS) generate more than one new point in parallel at each iteration. The parallel algorithms are tested on a Cray T3D parallel computer and speedups of up to 47 are achieved. Some comparison is also made with genetic algorithms. Initial results on a design optimization problem are given. Optimal design could serve to increase efficiency and reduce environmental pollution and thus, its economic importance cannot be overemphasized. There has been a lot of effort in this area, such as shape optimization of airfoils and artificial heart components, optimal design of structures for the reduction of vibrations, and optimal portfolio selection. The controlled random search algorithm is strongly related to the genetic algorithm. It would be useful to explore both algorithms by borrowing operators from the other. For instance, it would be interesting to use a tournament selection strategy in the PCRS(p, 1, q), and to include a mutation phase, in the hope of improving its reliability." @default.
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- W2071808953 date "1998-01-01" @default.
- W2071808953 modified "2023-09-23" @default.
- W2071808953 title "Parallel Controlled Random Search Algorithms for Shape Optimization" @default.
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- W2071808953 doi "https://doi.org/10.1016/b978-044482849-1/50041-5" @default.
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