Design of Space Trusses Using Big Bang–Big Crunch Optimization

Abstract

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 numerically simple algorithm with relatively few control parameters; and the ability to handle a mixture of both continuous and discrete design variables. Low-weight design and performance comparisons for several benchmark-type truss structures are presented between the BB-BC procedure and various classical and evolutionary optimization methods.