Monopulse antennas form an important methodology of realizing tracking radar. 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 of monopulse antennas in the framework of Multi-objective Optimization (MO). The synthesis problem is recast as an MO problem (for the first time, to the best of our knowledge), 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 approximated Pareto Fronts (PFs) are obtained for different number of elements and sub-arrays using a recently developed and very competitive Multi-Objective Evolutionary Algorithm (MOEA) called MOEA/D-DE that uses a decomposition approach for converting the problem of approximation of the PF into a number of single objective optimization problems. This algorithm employs Differential Evolution (DE), one of the most powerful real parameter optimizers in current use, as the search method. The quality of solutions obtained is compared with the help of the trade-off graphs (plots of the approximated PF) generated by MOEA/D-DE on the basis of the two objectives to investigate the dependence of the number of array-elements and the number of sub-arrays on the final solution. Then we find the best compromise solutions for 20 element arrays and compare the results with standard single-objective algorithms such as the Differential Evolution (DE) and Particle Swarm Optimization (PSO) and hybrid techniques like Hybrid Contiguous Partition Method (HCPM) that has been reported in literature so far for the synthesis problem. Our experimental results indicate the MOEA/D-DE yields much better final results as compared to the standard single-objective and hybrid approaches over all the test cases covered here.
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