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A distributed implementation of parallel genetic algorithm for slope stability evaluation

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Parallel processing, the method of considering many small tasks to solve one large problem, has emerged as a key enabling technology in modern computing. Parallel computers can be simply classified into shared memory systems and distributed memory systems. The shared memory computers have a global memory attached to a number of processors enabling several processors to work concurrently on different parts of the same computation. A different approach towards building large parallel computers is to connect several processors via a network. Each processor has its own local memory. The cost of building these computers increases with the number of processors. The distributed memory multiprocessor systems are scalable over a wider range than the shared memory computers. There are many intermediate computer architectures, each with its distinct programming model. Common between them is the notion of message passing. In all parallel processing, data must be exchanged between cooperating tasks. Several research groups have developed software packages such as Parallel Virtual Machine (PVM), theMessage Passing Interface (MPI), and others. In this paper, hardware implementation of parallel information processing is introduced by application of a multicellular computer idea, in which working cells were composed of general purpose one-chip microcomputers. The influence of the cellular computer's structure size on quality and efficiency of calculations was analyzed. The optimal structure consisted of 4x4 cells which guaranteed achieving satisfactory recurrence of results for an assumed set of working parameters. This paper presents an idea and the results of trial computations regarding the problem of slope stability evaluation by variational calculus assisted by genetic algorithm.
Rocznik
Strony
13--26
Opis fizyczny
Bibliogr. 21 poz., rys., wykr.
Twórcy
autor
  • Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, psrok@uwm.edu.pl
Bibliografia
  • [1] R. Baker, M. Garber. Variational approach to slope stability. Proceedings of 9th International Conference on Soil Mechanics and Foundation Engineering, Tokyo, Japan 1977, pp. 9-12.
  • [2] W.F. Chen, X.L. Liu. Limit analysis in soil mechanics. Developments in Geotechnical Engineering, 52, Elsevier, Amsterdam-Oxford-New York-Tokyo 1990.
  • [3] R. Baker. Variational Slope Stability Analysis of Materials with Nonlinear Failure Criterion. Electronic Journal of Geotechnical Engineering, pp. 2005-0514, 2005.
  • [4] D. Leshchinsky, A.J. Reinschmidt. Stability of membrane reinforced slopes. Journal of Geotechnical Engineering ASCE, 111(11): 1285-1300, 1985.
  • [5] P. Lemonnier, A.H. Soubra, R. Kastner. Variational displacement method for geosynthetically reinforced slope stability analysis: Local stability. Geotextiles and Geomembranes, 16: 1-25, 1998.
  • [6] P. Lemonnier, A.H. Soubra, R. Kastner. Variational displacement method for geosynthetically reinforced slope stability analysis. II: Global stability. Geotextiles and Geomembranes, 16: 27-44, 1998.
  • [7] P. Srokosz. Slope stability analysis by variational method with genetic algorithm application. Part 1: Slopes with homogeneous cohesive soils. Simple genetic algorithm application. Archives of Civil Engineering, 53(1): 85-108, 2007.
  • [8] P. Srokosz. Slope stability analysis by variational method with genetic algorithm application. Part 2: Genetic algorithm with advanced techniques. Simple modeling of uncertainties. Archives of Civil Engineering, 53(2): 269-292, 2007.
  • [9] P. Srokosz. Slope stability analysis by variational method with genetic algorithm application. Part 3: Normal stress distribution. Parallel and distributed computing. Archives of Civil Engineering, 54(2): 423-441, 2008.
  • [10] M. Matsumoto, T. Nishimura. Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Transactions on Modeling and Computer Simulation, 8(1): 3-30, 1998.
  • [11] J. Arabas. Lectures on evolutionary algorithms [in Polish]. Wydawnictwa Naukowo-Techniczne, Warsaw 2004.
  • [12] S.K. Das. Slope stability analysis using genetic algorithm. Electronic Journal of Geotechnical Engineering, pp. 2005-0504, 2005.
  • [13] A.T.C. Goh. Genetic algorithm search for critical slip surface in multiplewedge stability analysis. Canadian Geotechnical Journal, 36: 382-391, 1999.
  • [14] P. McCombie, P. Wilkinson. The use of the simple genetic algorithm in finding the critical factor of safety in slope stability analysis. Computers and Geotechnics, 29: 699-714, 2002.
  • [15] N. Sabhahit, J. Sreeja, M.R. Madhav. Genetic algorithm for searching critical slip surface. Indian Geotechnical Journal, 32(2): 86-101, 2002.
  • [16] P. Srokosz. Slope stability analysis by variational method with genetic algorithm application. Part 4: Parallel genetic algorithms. Archives of Civil Engineering, 55(2): 229-256, 2009.
  • [17] Parallel Virtual Machine System (PVM) version 3.4. University of Tennessee, Knoxville TN; Oak Ridge National Laboratory, Oak Ridge TN; Emory University, Atlanta GA.
  • [18] D. Whitley. A genetic algorithm tutorial. Computer Science Department, Colorado State University, available online: http://samizdat.mines.edu/ga_tutorial/ (accessed 21.07.2009).
  • [19] E. Alba, J.M. Troya. Improving flexibility and efficiency by adding parallelism to genetic algorithm. Statistics and Computing, 12: 91-114, 2002.
  • [20] D.E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, 1989.
  • [21] E. Alba, J.M. Troya. Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Generation Computer Systems 2001; 17: 451-465.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB8-0017-0007
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