Parallel simulated annealing for structural optimization

Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method

“…The minimum cross-sectional area of all design variables is 0.1 inch 2 . The structure is designed against three independent loading conditions: (1) 1.0 kip acting in the positive x-direction at nodes 1,6,15,20,29,34,43,48,57,62, and 71; (2) 10.0 kips acting in the negative y-direction at nodes 1,2,3,4,5,6,8,10,12,14,15,16,17,18,19,20,22,24,26,28,29,30,31,32,33,34,36,38,40,42,43,44,45,46,47,48,50,52,54,56,57,…”

“…One of the main disadvantages of the deterministic global optimization methods is the high-dimensionality issue caused by the larger number of design variables [3] or constraints. In contrast, another branch of gradient-free methods (or stochastic optimization methods), such as Genetic Algorithms (GA) [6][7][8][9][10][11][12], Simulated Annealing (SA) [13][14][15],…”

Semantically enabled process synthesis and optimisation

“…superstructure representation (Yeomans and Grossman 1999;Linke and Kokossis 2003a), whereas attempts were made to improve slow convergence of deterministic optimisation by simplifying the conceptual content and hence analytics (Raman and Grossmann 1991;Bauer and Stichlmair 1996). Similarly, slow convergence of stochastic searches was addressed by parallelising the execution and hence providing larger number of solutions in shorter time (Talbi et al 1998;Leite and Topping 1999;Wang, ZG et al 2005;James et al 2009;Kokossis, A et al 2011;Kim et al 2012;Cecelja, F et al 2014). Interpretation and assuring the confidence in obtained results, however, still remains a problem.…”

An Application of Simulated Annealing to the Optimum Design of Reinforced Concrete Retaining Structures

“…To control this decrement a minimum number of transitions that should be accepted at each temperature together with the minimum acceptance ratio are established within the algorithm. The temperature decrement is given by Tk+1 = αc Tk (13) where Tk and Tk+1 are the system temperatures at k and k+1 iteration. …”

“…Examples of application of simulated annealing to specific optimisation problems are also reported, such as Topping et al (1996), who used a simulated annealing algorithm to perform changes in structural truss topology whist minimising the member cross-sectional areas, outlining the approach to optimisation over an N-dimensional space of continuous values for the member cross-sectional areas as a discrete set of chosen values. In their later work, to overcome the algorithm's major disadvantage in solving a complex systems (potentially extremely slow convergence process), Leite and Topping (1999) suggested that a more efficient way to reduce the processor time and make the simulated annealing algorithms a more attractive solution for engineering problems is to add parallelism. Furthermore, their paper provides guidelines for the selection of appropriate parallel schemes for engineering problems.…”