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- W3215777744 abstract "This paper proposes a design-driven structural optimization algorithm named ε-constraint guided stochastic search (ε-GSS) for multi-objective design optimization of large-scale steel double-layer grids having numerous discrete design variables. Based on the well-known ε-constraint method, first, the multi-objective optimization problem is transformed into a set of single-objective optimization problems. Next, each single-objective optimization problem is tackled using an enhanced reformulation of the standard guided stochastic search algorithm proposed based on a stochastic maximum incremental/decremental step size approach. Moreover, a successive seeding strategy is employed in conjunction with the proposed ε-GSS algorithm to improve its performance in multi-objective optimization of large-scale steel double-layer grids. The numerical results obtained through multi-objective optimization of three challenging test examples, namely a 1728-member double-layer compound barrel vault, a 2304-member double-layer scallop dome, and a 2400-member double-layer multi-radial dome, demonstrate the usefulness of the proposed ε-GSS algorithm in generating Pareto fronts of the foregoing multi-objective structural optimization problems with up to 2400 distinct sizing variables." @default.
- W3215777744 created "2021-12-06" @default.
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- W3215777744 date "2022-04-01" @default.
- W3215777744 modified "2023-09-29" @default.
- W3215777744 title "ε-constraint guided stochastic search with successive seeding for multi-objective optimization of large-scale steel double-layer grids" @default.
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- W3215777744 doi "https://doi.org/10.1016/j.jobe.2021.103767" @default.
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