A Hybrid Particle Swarm and Ant Colony Optimization for Design of Truss Structures

This paper presents a particle swarm ant colony optimization for design of truss structures. The algorithm is based on the particle swarm optimizer with passive congregation and ant colony optimization. The particle swarm ant colony optimization applies the particle swarm optimizer with passive congregation for global optimization and ant colony approach is employed to update positions of particles to attain rapidly the feasible solution space. Ant colony optimization works as a local search, wherein, ants apply pheromone-guided mechanism to update the positions found by the particles in the earlier stage. A new relation is defined for the inertia weight, and the terminating criterion is changed in the way that after decreasing the movements of particles, the search process stops. With these changes, the number of iterations does not increase. The proposed method is tested on several benchmark trusses from literature. The result comparisons with particle swarm optimizer, particle swarm optimizer with passive congregation and other optimization algorithms demonstrate the effectiveness of the presented method.