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Vectorization converts classical scalar optimization formulation, which strictly separates the objective from constraints, into a vector-based optimization, transforming constraints into objectives. Effectively, the search is not conducted anymore for a single optimum, but for a set of Pareto optima between the original objective and transformed constraints. Constraint grouping enhances handling of multiple constraints for vectorized problems, by combining several constraints within a single-objective function, thus reducing the computational time and computational difficulties of high-dimensional spaces created by vectorization. This paper formulates and investigates these two concepts with respect to design of marine structures. It analyses their effects on the possibility to improve the flexibility of optimization in a practical environment, by implementing them within a simple genetic algorithm. Obtained results of vectorization applied to realistic weight optimization problem are encouraging when compared with the results of the classical scalar form optimization, showing a significant improvement in magnitude as well as in reduced computation time needed to reach the optimum. |
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