Discrete Optimization Problems of Linear Array Synthesis by Using Real Number Particle Swarm Optimization

Abstract

Abstract-It is generally believed genetic algorithm (GA) is superior to particle swarm optimization (PSO) while dealing with the discrete optimization problems. In this paper, a suitable mapping method is adopted and the modified PSO can effectively deal with the discrete optimization problems of linear array pattern synthesis. This strategy has been applied in thinned linear array pattern synthesis with minimum sidelobe level, 4-bit digital phase shifter linear array pattern synthesis and unequally spaced thinned array pattern synthesis with minimum sidelobe level. The obtained results are all superior to those in existing literatures with GA, iterative FFT and different versions of binary PSO, that show the effectiveness of this strategy and its potential application to other discrete electromagnetic optimization problems.

PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming

“…Sidelobe suppression problem has been widely discussed by researchers in the context of centralized antenna array [7][8][9][10][11][12][13][14][15][16][17][18][19]. Metaheuristic algorithms such as genetic algorithm (GA) [14], particle swarm optimization (PSO) [17,16,15] and evolutionary algorithm (EA) [18,11,19] are popular approaches that has been undertaken to solve this problem in the past.…”

“…Metaheuristic algorithms such as genetic algorithm (GA) [14], particle swarm optimization (PSO) [17,16,15] and evolutionary algorithm (EA) [18,11,19] are popular approaches that has been undertaken to solve this problem in the past. However, most of these researches exploit the array's geometry to achieve beampattern with reduced sidelobe, whereas position of the nodes in CB scenarios usually cannot be arranged.…”

A Pareto Elite Selection Genetic Algorithm for Random Antenna Array Beamforming With Low Sidelobe Level

“…A popular method in the literature to reduce the SLL in antenna arrays is by strategically placing the elements according to a set of pre-calculated optimum positions using optimization algorithms such as Genetic Algorithms (GA) [14,20,21], Particle Swarm Optimization [14,20,21,26], Differential Evolution [27][28][29][30][31] and convex optimization [10,12]. The position of the elements in the array is optimized via iterative procedures until the cost function of the algorithm, which is the PSLL, is minimized.…”

A Hybrid Deterministic/Metaheuristic Synthesis Technique for Non-Uniformly Spaced Linear Printed Antenna Arrays

“…In this study, attention has been put on the PSO method. The PSO is a robust stochastic evolutionary optimization method based on swarm intelligence [8], and has been successfully applied in the solution of electromagnetic problems regarding antenna design [9][10][11][12].…”

“…In this way, the initial array element locations and excitation distributions useful to mimic a given radiation pattern can be determined analytically while addressing design constraints relevant to the minimum antenna spacing, array aperture, and maximum number of power levels to be operated in the array beam-forming network. The obtained array configuration is afterwards processed within a dedicated Particle Swarm Optimization (PSO) algorithm [8][9][10][11][12] in combination with a multiport network approach [13,14] for computationally efficient modeling of the parasitic mutual coupling between the antenna elements. Along these lines, an enhancement of the convergence in terms of the number of iterations within the metaheuristic procedure and, consequently, a reduction of the total computational time required to obtain a converged solution of problem can be achieved.…”