Engineering Structural Optimization with an Improved Ant Colony Algorithm

Abstract

Ant colony optimization (ACO) algorithm is a newly developed bionics method, which has been successfully applied to several kinds of optimization problems as the traveling salesman problem, sequential ordering, and management of communications networks and so on. In this paper the ACO algorithms for optimization problems of engineering structure as the antenna structure was presented. It was shown how to construct the responding relations between the ACO algorithms and structural optimization problems. For improving the performance of ACO algorithms, several parameters of the algorithm, particularly the pheromone evaluation, had been improved here. Three examples on structural optimization were presented and solved by the improved ACO algorithms, Genetic Algorithm, Simulated Annealing Algorithm and so on. The comparisons between the improved ACO algorithm and other algorithm for the three examples have been obtained in terms of efficiency and effectiveness. The comparisons show the improved ACO algorithm is a very effective approach for solving structural optimization problems. And the improved ACO algorithm can greatly enforce the robustness of the optimal result.