Modification of Real-Number and Binary PSO Algorithms for Accelerated Convergence

Abstract

Modifications in the velocity calculation of the particle swarm optimization (PSO) algorithm are proposed. The suggested modifications aim to arrive at a faster, more straightforward and still robust search procedure as compared to the conventional method. Two main factors, i.e., personal best influence and initial velocity values, are evaluated. It is shown that in problems with wide-range parameters, the effect of personal best locations is intrinsically encompassed by that of global best locations, thereby allowing for further simplification of the PSO algorithm by eliminating the factor which accounts for the personal best solutions in the velocity calculation. This simplification expedites the convergence procedure in real PSO. It is also shown that the initial velocity values can be modified to enhance the performance in terms of achieving better solution when compared with the existing algorithms, particularly in binary PSO. In order to validate the viability of the proposed procedure, the performances of the real-number and binary PSO algorithms with different velocity calculations are assessed in 1000-run sets, and pros and cons are studied. In particular, the performance of the proposed algorithm, when used to design software defined thinned array antennas, is shown to be superior to those of the existing algorithms.