Constrained parallel adaptive surrogate model optimization algorithm and its application in optimal design of radial gates
Aiming at the optimization design problem of complex large-scale engineering structure under parallel sim-ulation technology,combining adaptive surrogate model optimization and computer parallel computing technology,a surrogate model optimization algorithm based on constrained parallel adaptive sampling was proposed.The sampling method of the algorithm was composed of a local minimum model prediction single-point strategy and a global dual-objective constraint multi-point strategy.The sample points were selected by constructing the constraint expectation improvement function and the sample space sparsity function,so that the obtained new sample points had the ability to balance the search for the local optimal region of the objective function and the development of the global feasible boundary.The comparison and analysis of test examples and existing algorithms showed that the algorithm had bet-ter optimization efficiency,optimization accuracy and stability.Finally,the algorithm was applied to the multi-pa-rameter optimization of the large steel structure radial gate,and three kinds of adaptive surrogate model optimization algorithms and genetic algorithm based on static surrogate model were used to solve the problem re-spectively.The results showed that the proposed algorithm had achieved a more significant optimization effect under the conditions of working performance and safety,the gate quality reduced by 42.85％,which maximized the mate-rial performance and saved the gate cost.
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维普
