A design procedure utilizing an ant colony optimization (ACO) technique is developed for discrete optimization of space trusses. The objective function considered is the total weight (or cost) of the structure subjected to material and performance constraints in the form of stress and deflection limits. The design of space trusses using discrete variables is transformed into a modified traveling salesman problem (TSP) where the network of the TSP reflects the structural topology and the length of the TSP tour is the weight of the structure. The resulting truss, mapped into a TSP, is minimized using an ACO algorithm. The ACO design procedure uses discrete design variables, an open format for prescribing constraints, a penalty function to enforce design constraints, and allows for multiple loading cases. A comparison is presented between the ACO truss design procedure and designs developed using a genetic algorithm and classical continuous optimization methods.

A procedure for designing low-weight space trusses based on the innovative Big Bang–Big Crunch (BB–BC) optimization method is developed for both discrete and continuous variable optimization. BB-BC optimization is a population-based heuristic search consisting of two parts: The Big Bang where candidate solutions are randomly distributed over the search space; and a Big Crunch where a contraction operation estimates a weighted average or center of mass for the population. Each sequential Big Bang is then randomly distributed about the center of mass. The objective of the optimization is to minimize the total weight (or cost) of the structure subjected to material and performance constraints in the form of stress and deflection limits. Designs are evaluated for fitness based on their penalized structural weight, which represents the actual truss weight and the degree to which the design constraints are violated. BB-BC optimization has several advantages over other evolutionary methods: Most significantly, a...

A design procedure utilizing an ant colony optimization (ACO) technique is developed for discrete optimization of steel frames. The objective function considered is the total weight (or cost) of the structure subjected to serviceability and strength requirements as specified by the American Institute for Steel Construction (AISC) Load and Resistance Factor Design, 2001. The design of steel frames is mapped into a modified traveling salesman problem (TSP) where the configuration of the TSP network reflects the structural topology, and the resulting length of the TSP tour corresponds to the weight of the frame. The number of potential paths between nodes in the TSP network represents all (or a portion) of the available W-shapes in the AISC database. The resulting frame, mapped into a TSP, is minimized using an ACO algorithm with a penalty function to enforce strength and serviceability constraints. A comparison is presented between the ACO frame designs and designs developed using a genetic algorithm and cl...

In this paper we present a genetic algorithm (GA)-based optimization procedure for the design of 2D, geometrical, nonlinear steel-framed structures. The approach presented uses GAs as a tool to achieve discrete nonlinear optimal or near-optimal designs. Frames are designed in accordance with the requirements of the AISC-LRFD specification. In this paper, we employ a group selection mechanism, discuss an improved adapting crossover operator, and provide recommendations on the penalty function selection. We compare the differences between optimized designs obtained by linear and geometrically nonlinear analyses. Through two examples, we will illustrate that the optimal designs are not affected significantly by the P-Δ effects. However, in some cases we may achieve a better design by performing nonlinear analysis instead of linear analysis.

A design procedure implementing a genetic algorithm is developed for discrete optimization of reinforced concrete frames (RC-GA). The design procedure conforms to the American Concrete Institute (ACI) Building Code and Commentary. The objective of the RC-GA procedure is to minimize the material and construction costs of reinforced concrete structural elements subjected to serviceability and strength requirements described by the ACI Code. Examples are presented demonstrating the efficiency of the RC-GA procedure for the flexural design of simply-supported beams, uniaxial columns, and multi-story frames.

A procedure is developed for designing low-cost or low-weight cantilever reinforced concrete retaining walls, with base shear keys, using big bang–big crunch (BB-BC) optimization. The objective of the optimization is to minimize the total cost or total weight per unit length of the retaining structure subjected to constraints on the basis of stability, bending moment, and shear force capacities and the requirements of the American Concrete Institute (ACI 318-05). An iterative population-based heuristic search method, BB-BC optimization has a numerically simple algorithm with relatively few control parameters as compared with other evolutionary methods. Low-cost and low-weight designs for two retaining walls are presented. In addition, results are presented on the effects of surcharge load, backfill slope, and internal friction angle of the retained soil on the values of low-cost and low-weight designs with and without a base shear key.