Tri-Objective Compact Log-Periodic Dipole Array Antenna Design Using MOEA/D-GPSO

Abstract

Considering the defects of particle swarm optimization (PSO) and multi-objective evolutionary algorithm (EA) based on decomposition (MOEA/D), this article proposes an improved multi-objective EA MOEA/D-GPSO for compact log-periodic dipole array (LPDA) design. MOEA/D-GPSO decomposes multiple objectives into a number of single-objective optimization problems. Each particle deals with one sub-problem. All the particles are divided into a few groups, and each particle has several neighboring particles. During the search, a new solution is constructed by learning information from the random non-dominated solutions found by its own neighbors and group. ZDT instances have been introduced to verify the effectiveness of MOEA/D-GPSO with respect to other outstanding multi-objective EAs. Further, two miniaturized LPDA designs for the application of digital video broadcasting-terrestrial (DVB-T) (470–790 MHz), a tri-objective compact LPDA, and a novel LTE800-refused compact LPDA, respectively, are presented, showing their good performance over other similar designs and promising prospect of the proposed algorithm for high-dimensional and multi-functional LPDA design.