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The beam pattern synthesis of antenna arrays is one of the most significant optimization problems in the electromagnetics community, and it affects the performance of wireless communication systems. In this article, we formulate the beam pattern optimization problems, and propose an improved invasive weed optimization with random mutation and Le&#x0301;vy flight (IWORMLF) algorithm to synthesize the beam patterns of linear antenna arrays (LAAs) and circular antenna arrays (CAAs). IWORMLF introduces the random mutation operator and the Le&#x0301;vy flight mechanism to enhance the efficiency and to balance the exploration and exploitation of the algorithm for optimization problems. Several simulations are conducted to evaluate the performance of the proposed approach. First, IWORMLF is tested by a variety of benchmark functions, and the results indicate that it achieves the best performance in most of the functions compared to some other algorithms. Second, IWORMLF is utilized for the maximum sidelobe level (SLL) reduction and the joint maximum SLL and mainlobe beamwidth reductions. The results show that it achieves better performances in terms of the convergence rate, stability, and accuracy compared to some evolutionary algorithms for these optimization cases. Third, the efficiencies of the improved factors are verified. Finally, electromagnetism simulations are conducted to evaluate the performance of IWORMLF for the beam pattern optimizations with considering mutual coupling.

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IWORMLF: Improved Invasive Weed Optimization With Random Mutation and L&#x00E9;vy Flight for Beam Pattern Optimizations of Linear and Circular Antenna Arrays