Search Results (2)

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. Full article

The design of an effective antenna array is a major challenge encountered in most communication systems. A much-needed requirement is obtaining a directional and high-gain radiation pattern. This study deals with the design of a linear antenna array that radiates with reduced peak-side lobe levels (PSLL), decreases side-lobe average power with and without the first null beamwidth (FNBW) constraint, places deep nulls in the desired direction, and minimizes the close-in-side lobe levels (CSLL). The nature-inspired metaheuristic algorithm multi-verse optimization (MVO) is explored with other state-of-the-art algorithms to optimize the parameters of the antenna array. MVO is a global search method that is less prone to being stuck in the local optimal solution, providing a better alternative for beam-pattern synthesis. Eleven design examples have been demonstrated, which optimizes the amplitude and position of antenna array elements. The simulation results illustrate that MVO outperforms other algorithms in all the design examples and greatly enhances the radiation characteristics, thus promoting industrial innovation in antenna array design. In addition, the MVO algorithm’s performance was validated using the Wilcoxon non-parametric test. Full article