Layout optimisation of trusses using simulated annealing

This paper addresses to the development of a simulated annealing (SA) based solution algorithm for the simultaneous optimum design of truss type structures with respect to size, shape and topology design variables. The proposed algorithm is designed in such way that together with applicability to practical design problems, it also aims to produce efficient and improved design solutions for the problems of interest. From the practical point of view, the objective chosen is to minimise the weight of the structures under a set of particular constraints imposed by design code specifications on nodal displacement, member stress and stability. Concerning the efficiency of the algorithm, SA is adapted to be able to work fruitfully in the design spaces of complex problems occupied by many regions of highly different characteristics. The proposed algorithm is tested on two large design example problems taken from the literature for comparison purposes and the results are fully discussed.

[1]  D. Janaki Ram,et al.  Parallel Simulated Annealing Algorithms , 1996, J. Parallel Distributed Comput..

[2]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[3]  Jasbir S. Arora,et al.  OPTIMAL DESIGN WITH DISCRETE VARIABLES: SOME NUMERICAL EXPERIMENTS , 1997 .

[4]  P. Hajela.  Nongradient Methods in Multidisciplinary Design Optimization-Status and Potential , 1999 .

[5]  Zbigniew Michalewicz,et al.  A Survey of Constraint Handling Techniques in Evolutionary Computation Methods , 1995 .

[6]  Gun-Shing Chen,et al.  OPTIMAL PLACEMENT OF ACTIVE/PASSIVE MEMBERS IN TRUSS STRUCTURES USING SIMULATED ANNEALING , 1991 .

[7]  Alistair I. Mees,et al.  Convergence of an annealing algorithm , 1986, Math. Program..

[8]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[9]  Chris P. Pantelides,et al.  Annealing Strategy for Optimal Structural Design , 1996 .

[10]  O. Hasançebi,et al.  Layout optimization of trusses using improved GA methodologies , 2001 .

[11]  Jamshid Ghaboussi,et al.  Evolution of Optimum Structural Shapes Using Genetic Algorithm , 1998 .

[12]  Hans-Paul Schwefel,et al.  Evolution and Optimum Seeking: The Sixth Generation , 1993 .

[13]  T. Elperin,et al.  Monte Carlo structural optimization in discrete variables with annealing algorithm , 1988 .

[14]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[15]  O. Hasançebi,et al.  On efficient use of simulated annealing in complex structural optimization problems , 2002 .

[16]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[17]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[18]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[19]  Richard J. Balling,et al.  Optimal Steel Frame Design by Simulated Annealing , 1991 .

[20]  Souran Manoochehri,et al.  GENERATING OPTIMAL CONFIGURATIONS IN STRUCTURAL DESIGN USING SIMULATED ANNEALING , 1997 .

[21]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[22]  A. Dhingra,et al.  Single and multiobjective structural optimization in discrete‐continuous variables using simulated annealing , 1995 .