Distributed Genetic Algorithm for Structural Optimization

Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method

“…It can be easily proved that the Karush-Kuhn-Tucker solution (x * , λ * ) of the problem in Equation (6) is a solution to the problem in Equation (1) [26,43]. As a result, SSO for unconstrained optimization [44] can be applied to the problem in Equation (1) after it has been converted into the problem in Equation (6). It is also well-known that if the magnitude of penalty parameters is larger than a positive real value, the solution to the unconstrained problem is identical to that of the original constrained problem [43].…”

“…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],…”

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

“…Topping and Leite [80] utilized this parallel GA for the design optimization of a bridge, considering a number of constraints. Adeli and Kumar [81] used a network consisted of computer processors for optimization of largespaced steel structures. Sarma and Adeli [82] hybridized the coarse grained GAs with the fuzzy logic search method, for design optimization of three-dimensional frame structures.…”

Crew Allocation System for the Masonry Industry

“…Multiple techniques have been used to solve scheduling and allocation problems such as integer programming (Ipsilandis, 2007;Elazouni and Gab-Allah, 2004), multiobjective optimization (Thiel, 2008;Gao et al, 2012;Koo et al, 2016), genetic algorithms (Adeli and Kumar 1995;AlBazi and Dawood, 2010;Ponz-Tienda et al, 2015), simulation-based optimization (Horn et al, 2007;Chen and Shahandashti, 2009), stochastic simulation (Maxwell et al, 1998), dynamic programming (Dück et al, 2012), ranking methods (Lin 2011), tabu search algorithms (Erdogan et al, 2010), fuzzy models (Shahhosseini and Sebt, 2011), metaheuristics (Caprara et al 1998;Yunes et al, 2005;Debels et al, 2006), goal programming (Chu, 2007), non-linear programming (Klanšek, 2015), and stochastic programming (Morton and Popova, 2004;Lu et al, 2008).…”