A REFINEMENT OF DISCRETE PARTICLE SWARM OPTIMIZATION FOR LARGE-SCALE TRUSS STRUCTURES

This paper proposes a refined version of particle swarm optimization technique for the optimum design of steel structures. Swarm is composed of a number of particles and each particle in the swarm represents a candidate solution of the optimum design problem. Design constraints in accordance with ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution) are imposed by the particle swarm optimization based optimum design algorithm developed. A constraint handling method called the ‘penalty function method’ is introduced to maintain acceptable solutions. The refined version of the particle swarm optimization algorithm proposed in this paper is easy to implement and the results and convergence performance are better than the simple particle swarm optimization algorithm and some other meta-heuristic optimization techniques. The effect of different inertia weight parameters in finding the optimum design is also tested in two numerical examples.