Angel method for discrete optimization problems

Abstract

In this study we present 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. In this study we propose an efficient local search procedure. The procedure, in an iterative process, tries to make a better (a lighter feasible or a less unfeasible) truss from the current truss obtained by ACO or GA. The geometrically and materially nonlinear space trusses are formulated as a large d isplacement structural model. The treatment of elastic-plastic collapse analysis is based on a path-following method \cite 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 predictor-corrector terms, the method is able to follow the load- deflection path even in case of elastic-plastic material law.