A hybrid boundary condition for robust particle swarm optimization

The particle swarm optimization (PSO) technique is a powerful stochastic evolutionary algorithm that can be used to find the global optimum solution in a complex search space. However, it has been observed that there is a great variation in its performance due to the dimensionality of the problem and the location of the global optimum with respect to the boundaries of the search space. The present paper attempts to resolve this problem by proposing a simple hybrid "damping" boundary condition that combines the characteristics offered by the existing "absorbing" and "reflecting" boundaries. Simulation results on microwave image reconstruction have shown that with the proposed "damping" boundary condition, a much robust and consistent optimization performance can be obtained for PSO regardless of the dimensionality and location of the global optimum solution.