1996
DOI: 10.1016/0305-0548(95)00063-1
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Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems

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Cited by 148 publications
(76 citation statements)
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“…Therefore, traditional search techniques based on local linearization, which use characteristics of the problem to determine the next sampling point (e.g., gradients, Hessians, linearity, and continuity), are computationally efficient but fail to identify the best-fit solution when the starting model is too far from the global optimal solution. On the other hand, stochastic search techniques (e.g., genetic algorithms and simulated annealing) have been shown to efficiently identify promising regions in the search space, but their convergence rate has been shown to be poor in localized searches (Bersini and Renders, 1994;Houck et al, 1996).…”
Section: Stochastic Seismogram Inversion Algorithm With Deterministicmentioning
confidence: 99%
“…Therefore, traditional search techniques based on local linearization, which use characteristics of the problem to determine the next sampling point (e.g., gradients, Hessians, linearity, and continuity), are computationally efficient but fail to identify the best-fit solution when the starting model is too far from the global optimal solution. On the other hand, stochastic search techniques (e.g., genetic algorithms and simulated annealing) have been shown to efficiently identify promising regions in the search space, but their convergence rate has been shown to be poor in localized searches (Bersini and Renders, 1994;Houck et al, 1996).…”
Section: Stochastic Seismogram Inversion Algorithm With Deterministicmentioning
confidence: 99%
“…For example, the various versions of variable neighborhood search in the above comparative study use Cooper's algorithm in the local search step. The initial population in the genetic algorithm of Houck et al (1996) is obtained by repeating Cooper's algorithm from random starting points until an adequate number of local minima is found, and after the crossover operation, the new solution is improved (mutation operation) using the Cooper algorithm. For a further update on metaheuristic-based methods for solving the continuous p-median problem, see Brimberg et al (2008a).…”
Section: The Continuous P-median Problemmentioning
confidence: 99%
“…In particular, a nonlinear Gauss-Newton scheme is employed, forcing the active parental generation to convergence to local minima or maxima prior to mutation, crossover, and reproduction. This technique, referred to as the hill-climbing method of local optimization, has been shown to significantly enhance the performance of genetic algorithms (Stoffa and Sen, 1991;Houck et al, 1996). The objective function of the nonlinear least-squares optimization is defined in the frequency domain, as the energy error between the model and data vectors:…”
Section: CMmentioning
confidence: 99%
“…This optimization technique comprises a genetic algorithm in the wavelet domain coupled to a nonlinear least-squares fit in the frequency domain, and it has been shown to improve the computational efficiency of the former while avoiding the pitfalls of using local linearization techniques such as the latter (Houck et al, 1996). The parameters estimated are stepwise variations of the shear-wave velocity, attenuation, and density with depth for horizontally layered media with predefined layer thickness.…”
Section: Introductionmentioning
confidence: 99%