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- W4313135904 abstract "AbstractA two steps based approach for solving constrained engineering and numerical optimization problems is introduced in this article. The proposed hybrid method called GA-MAL leverages the ability of genetic algorithm (GA) to reach a global optimum in problems involving complex design spaces while simultaneously enforcing the constraints using a modified augmented Lagrangian (MAL) multiplier method. The combined strategy consists of an outer iteration, in which the Lagrangian multipliers and several penalty settings are updated using a first-order update scheme, and an inner iteration, in which a nonlinear optimization of the MAL multiplier with straightforward constraints on the boundary is carried out through the GA process. The experiments on numerical constraint and surveillance radar (SR) coverage show that the proposed method is significantly competitive with current state-of-the-art algorithms.KeywordsOptimizationConstraint problemsAugmented LagrangianGenetic algorithmSurveillance radar" @default.
- W4313135904 created "2023-01-06" @default.
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- W4313135904 date "2022-01-01" @default.
- W4313135904 modified "2023-09-27" @default.
- W4313135904 title "Augmented Lagrangian Genetic Algorithm Approach Towards Solving Constrained Numerical and Coverage Optimization" @default.
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- W4313135904 doi "https://doi.org/10.1007/978-3-031-12097-8_21" @default.
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