Optimal Truss Design Using Ant Colony Optimization

The paper presents a methodology to arrive at optimal truss designs using Ant Colony Optimization (ACO) algorithms. Ant Colony Optimization is a population-based, artificial multi-agent, general-search technique for the solution of difficult combinatorial problems with its theoretical roots based on the behavior of real ant colonies and the collective trail-laying and trail-following of its members in searching for optimal solutions in traversing multiple paths. In essence, ACO is inspired by the foraging behavior of natural ant colonies which optimize their path from an origin (ant nest) to a destination (food source) by taking advantage of knowledge acquired by ants that previously traversed the possible paths and the pheromone trail these ants leave behind as the traverse the paths to optimal solution. In computer implementations of the ACO algorithms, artificial ants are agents and solution-construction procedures that stochastically build solutions by considering (1) artificial pheromone trails which change dynamically at run time to reflect the agents’ acquired search experience, and (2) heuristic information on the problem/network being solved. The paper outlines the fundamental mathematical background of the ACO method and a suggested possible implementation strategy for solving for optimal truss designs (geometrical configuration and member characteristics).