A New Hybrid Meta-Heuristic Method for Optimal Design of Space Trusses with Elastic-Plastic Collapse Constraints

Abstract

Within the framework of the finite element method, we present in this paper an efficient new hybrid meta-heuristic - named in other context ANGEL - for solving discrete size optimization of truss structures. ANGEL combines ant colony optimization (ACO), genetic algorithm (GA) and local search (LS) strategy. The procedures of ANGEL attempt to solve an optimization problem by repeating the following steps. First time, ACO searches the solution space and generates structure designs to provide the initial population for GA. After that, GA is executed and the pheromone set in ACO is updated when GA obtains a better solution. When GA terminates, ACO searches again by using the new pheromone set. ACO and GA search alternately and cooperatively in the solution space. This study also proposes an efficient local search procedure, which is applied to yield a better solution when ACO or GA obtains a solution. In this paper we applied ANGEL for discrete minimal weight design of space trusses with elastic-plastic collapse constraints. The geometrically and materially nonlinear space trusses are formulated as a large displacement structural model. The method of elastic-plastic collapse analysis is based on a path-following method [6]. The applied method is a combination of the perturbation technique of the stability theory and the non-linear modification of the classical linear homotopy method. With the help of the higher-order predictorcorrector terms, the method is able to follow the load- deflection path even in case of elastic-plastic material law.