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A genetic algorithm is used as a global search technique for the minimisation.
PSO is a global search technique originally introduced by Kennedy and Eberhart [33].
GA is a randomized global search technique that solves problems imitating processes observed from natural evolution.
The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM.
GA is a stochastic global search technique for optimizing an objective function with linear or non-linear constraints.
The highly nonlinear nature of the optimization problem demands the use of an initial parameterization independent global search technique.
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This method relies on both local search and global search techniques.
The optimization of the filter parameters and ANN architectures is carried out through global search techniques.
Joon-Min Gil and Youn-Hee Han, in the literature [10], proposed a goal based on genetic algorithm coverage scheduling scheme, using evolutionary global search techniques for monitoring all targets and that can find the optimal coverage set, prolonging the network life cycle.
Some researchers also suggested that global search techniques including the GA might prevent ANN from falling into a local minimum (McInerney and Dhawan, 1993; Sexton et al., 1998; Gupta and Sexton, 1999; Tahmasebi and Hezarkhani, 2009).
The identification procedure employs a global searching technique using Genetic Algorithms, and there is no restriction on the location, severity and the number of cracks to be identified.
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