A Study on the Particle Swarm Optimization with Adaptive Weight Constrained Layout Optimization

The layout optimization problem with performance constraints belongs to NP-Hard problem, which is usually difficult to solve. To balance the PSO algorithm's global search capability and the ability of local improvement, this paper presented an adaptive weight PSO algorithm, which introduced a nonlinear dynamic inertia weight coefficient. Given the low efficiency of the algorithm in dealing with constraints, this paper presented an improved model and created an optimization process which was divided into two steps, thereby obtaining a solution that both to meet the geometric constraints and converge to minimum of static non-equilibrium. The results of three examples showed that this algorithm was feasible and efficient.