MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design

This paper presents the modified arithmetic optimization algorithm (MAOA), a swift and effective optimization algorithm specifically designed for electromagnetic applications. Its primary advantage is its ability to avoid local minima by striking a balance between global exploration and local exploitation searches. This equilibrium is maintained through three key improvements: an enhanced initialization process, a distinctive guidance mechanism for steering searches, and an additional learning phase to refine newly found solutions. This process innovation significantly boosts MAOA’s performance in addressing both constrained and unconstrained optimization challenges. In this study, MAOA is applied to optimize the spacing and current amplitude of linear antenna array (LAA) elements, with the goal of minimizing peak side lobe level (PSLL), close-in side lobe level (CSLL), and overall side lobe level (SLL), both with and without constraints on first null beamwidth (FNBW), as well as null positioning with SLL minimization. Ten designs, comprising 10 and 20 antenna elements of LAA and one 14-element circular antenna array (CAA), showcase MAOA’s proficiency in antenna array pattern synthesis. Optimizing element positions results in a PSLL of −21.28 dB, a CSLL of −34.50 dB, and a null depth of −89.00 dB, while optimizing current amplitude achieves a PSLL of −24.32 dB, a CSLL of −29.73 dB, and a null depth of −77.60 dB across various antenna designs. Simulation results reveal that MAOA significantly surpasses traditional uniform linear arrays (ULA) and established optimization techniques. Its superiority is further confirmed through a Wilcoxon rank-sum and Friedman test.