Showing 2 results for Genetic Algorithm.
M. H. Bagheripour, E. Shasavandi, and S. M. Marandi,
Volume 25, Issue 2 (1-2007)
Abstract
This paper introduces an accurate, fast, and applicable method for optimization of slip surfaces in earth slopes. Using Genetic Algorithm (GA), which is one of the modern and non-classic optimization methods, in conjunction with the
well -known Bishop applied method, the optimum slip surface in an earth slope is investigated and its corresponding lowest safety factor is determined. Investigations have shown that selection of appropriate variables to define and to solve the problem and determination of a good range for these variables have a profound effect on the speed of convergence in the problem. In the present study, appropriate variables have been defined for solving the problem in a way that the number of repetitions required to reach convergence are considerably reduced by up to 50% compared with other approaches. This has led to a drastic reduction in time and the memory required. The accuracy of the method is shown first by solving examples related to search for optimum failure surfaces of some homogenous, non-homogenous, and earth dam slopes and then by comparison of the results with those of other optimization techniques. In order to show the application of the present method in modern geotechnical engineering, a reinforced earth slope is studied and its failure surface is finally optimized
M. Rabbani, F. Taghiniam, H. Farrokhi-Asl , H. Rafiei ,
Volume 35, Issue 2 (2-2017)
Abstract
In this paper, the solution of a non-linear model of Cell Manufacturing (CM) in certain and dynamic conditions is
studied, considering intracellular and extracellular costs, cell constructing costs, the cost of restoration and the cost of equipment
transportation per distance travelled. Since the number of cells in each stage of production is important, by optimizing the
number of cells, additional costs can be minimized. Therefore, the main objective of this study is to investigate the optimal
number of cells located. Bio-geographical Based Optimization (BBO) algorithm is applied in the CM for the first time in the
literature and the obtained results from this algorithm are compared with the results of well-known genetic algorithm. The results
shows the good performance of genetic algorithm. Finally, the conclusion and future research are provided.
