Multi-objective design of monopulse antenna with Two-lbests based multi-objective particle swarm optimizer

Monopulse antennas form an important methodology of realizing tracking radar and they are based on the simultaneous comparison of sum and difference signals to compute the angle-error and to steer the antenna patterns in the direction of the target (i.e., the boresight direction). In this study, we consider the synthesis problem of difference patterns in monopulse antennas from the perspective of Multi-objective Optimization (MO). The synthesis problem is recast as an MO problem, where the Maximum Side-Lobe Level (MSLL) and Beam Width (BW) of principal lobe are taken as the two objectives to be minimized simultaneously. The approached Pareto Fronts are obtained for different number of elements and subarrays using the Two-lbests based multi-objective particle swarm optimizer (2LB-MOPSO). The quality of solutions obtained is compared with the help of Pareto Fronts on the basis of the two objectives to investigate the dependence of the number of elements and the number of sub-arrays on the final solution. Then we find the best compromise solutions for 20 element array and compare the results with standard single objective Particle Swarm Optimization (PSO) that has been reported in literature so far for the synthesis problem. Our experimental results indicate the 2LB-MOPSO yields much better final results as compared to the standard single-objective approach over all considered test cases.

[1]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[2]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[3]  L. Jain,et al.  Evolutionary multiobjective optimization : theoretical advances and applications , 2005 .

[4]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[5]  Derek A. McNamara,et al.  Synthesis of sum and difference patterns for two-section monopulse arrays , 1988 .

[6]  E. Moreno,et al.  Subarray weighting for the difference patterns of monopulse antennas: joint optimization of subarray configurations and weights , 2001 .

[7]  Ponnuthurai N. Suganthan,et al.  Multiobjective Particle Swarm optimizer with dynamic epsilon-dominance sorting , 2010, 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC).

[8]  Ponnuthurai Nagaratnam Suganthan,et al.  Two-lbests based multi-objective particle swarm optimizer , 2011 .

[9]  A. Massa,et al.  Optimization of the Directivity of a Monopulse Antenna With a Subarray Weighting by a Hybrid Differential Evolution Method , 2006, IEEE Antennas and Wireless Propagation Letters.

[10]  C.L. Dolph,et al.  A Current Distribution for Broadside Arrays Which Optimizes the Relationship between Beam Width and Side-Lobe Level , 1946, Proceedings of the IRE.

[11]  R. Elliott Antenna Theory and Design , 2003 .

[12]  A. Massa,et al.  Optimization of the difference patterns for monopulse antennas by a hybrid real/integer-coded differential evolution method , 2005, IEEE Transactions on Antennas and Propagation.

[13]  Samuel M. Sherman,et al.  Monopulse Principles and Techniques , 1984 .

[14]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[15]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[16]  E. Bayliss Design of monopulse antenna difference patterns with low sidelobes , 1968 .

[17]  Ponnuthurai N. Suganthan,et al.  Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection , 2010, Inf. Sci..

[18]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[19]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.