Truss weight minimization using hybrid Harmony Search and Big Bang-Big Crunch algorithms

Weight minimization of truss structures is an important topic of structural engineering. Metaheuristic optimization methods can explore the whole design space by performing a limited number of structural analyses. “Second generation” algorithms like harmony search (HS) and Big Bang–Big Crunch (BB–BC) are very efficient in truss optimization problems but are computationally expensive. To overcome this limitation, this chapter presents two novel hybrid formulations of HS and BB–BC where metaheuristic search is hybridized by including gradient/pseudogradient information as the criterion to accept or reject new trial designs or to perform new explosions. Each trial design is formed by combining a set of descent directions and then eventually corrected to improve it further. An improved local one-dimensional search derived from simulated annealing is included in the optimization process. The new HS and BB–BC algorithms are tested in the large-scale weight minimization problem of a space tower with 3586 elements and 280 design variables. The classical sizing optimization problem formulated for the planar 200-bar truss subject to five independent loading conditions is taken as the benchmark test to analyze sensitivity to population size. Numerical results prove the efficiency and robustness of the optimization algorithms developed in this research.