Quantum Particle Swarm Optimization for Synthesis of Non-uniformly Spaced Linear Arrays with Broadband Frequency Invariant Pattern

Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm

“…Figure 3 illustrates the advantages of the proposed cost function, where the red areas represent the cost values, i.e., the difference between the target pattern and the generated pattern, of the cost functions. Figure 3 a illustrates an example of a conventional cost function (used in [ 27 ]), which consists of a main pencil beam and a flat side-lobe. Hence, the cost is affected by the shape of the design main pencil beam.…”

“…The spacing and weights of a wideband NUSLA can be optimized by employing various heuristic optimization algorithms, such as firefly algorithm (FA) [ 19 ], salp swarm algorithm (SSA)[ 21 ], and quantum particle swarm optimization (QPSO) [ 27 ]. However, these heuristic optimization algorithms face a significant drawback in that convergence to the global optimum cannot be ensured, despite the trade-off between the convergence time and the chance to achieve the global optimum.…”

“…However, these heuristic optimization algorithms face a significant drawback in that convergence to the global optimum cannot be ensured, despite the trade-off between the convergence time and the chance to achieve the global optimum. Parameter optimization was performed for the QPSO in [ 27 ] via trial-and-error to prevent falling into a local optimum. However, the performance of QPSO is considerably affected by the hyper-parameter setting, which is typically optimized by trial-and-error on a case-by-case basis (such as scanning angles and element size).…”

Design of Non-Uniformly Spaced Circular Arrays of Parasitic Dipoles for Lower Side Lobe Level with Maximum Directivity

“…Matlab based method of moments (MoM) [14][15][16] code is used for simulation and to evaluate the performance of the antenna designs using optimization process generated by quantum particle swarm optimization (QPSO) [17][18]. Here, we have used QPSO algorithm because it provides better results than other algorithms in many antenna design problems [19][20][21][22]. QPSO perform better than PSO and its different versions on well-known benchmark functions [17][18].…”

“…QPSO provides better results than backtracking search algorithm (BSA) in linear antenna array failure correction to obtain the fixed value of side lobe level and VSWR [19]. Furthermore, it performs better in non-uniformly spaced linear antenna array problems using amplitude excitations and element positions than firefly algorithm (FA) [20]. QPSO also provides better results in synthesis of nonuniformly spaced linear array of unequal length parallel dipole antennas than PSO for impedance matching with low side lobe level and main lobe tilting including uniform null filling, generally used in broadcasting applications [21] and provides better results than FA and cuckoo search algorithm (CS) to achieve the low side lobe level with impedance matching [22].…”