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- W2078161768 abstract "Parallel algorithms for optimization of structures reported in the literature have been restricted to shared-memory multiprocessors. This paper presents a distributed genetic algorithm for optimization of large structures on a cluster of workstations connected via a local area network (LAN). The selection of genetic algorithm is based on its adaptability to a high degree of parallelism. Two different approaches are used to transform the constrained structural optimization problem to an unconstrained optimization problem: a penalty-function method and augmented Lagrangian approach. For the solution of the resulting simultaneous linear equations the iterative preconditioned conjugate gradient (PCG) method is used because of its low memory requirement. A dynamic load-balancing mechanism is developed to account for the unpredictable multiuser, multasking environment of a networked cluster of workstations, heterogeneity of machines, and indeterminate nature of the interative PCG equation solver. The algorithm has been applied to optimization of a large space steel structure subjected to vertical and horizontal loads and the constraints of the AISC ASD specifications." @default.
- W2078161768 created "2016-06-24" @default.
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- W2078161768 date "1995-07-01" @default.
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- W2078161768 title "Distributed Genetic Algorithm for Structural Optimization" @default.
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- W2078161768 doi "https://doi.org/10.1061/(asce)0893-1321(1995)8:3(156)" @default.
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