Antenna arrays with high directivity and low side lobe levels need to be designed for increasing the efficiency of communication systems. A new evolutionary technique, cat swarm optimization (CSO), is proposed for the synthesis of linear antenna arrays. The CSO is a high performance computational method capable of solving linear and non-linear optimization problems. CSO is applied to optimize the antenna element positions for suppressing side lobe levels and for achieving nulls in desired directions. The steps involved in the problem formulation of the CSO are presented. Various design examples are considered and the obtained CSO based results are validated by comparing with the results obtained using particle swarm optimization (PSO) and ant colony optimization (ACO). The flexibility and ease of implementation of the CSO algorithm is evident from this analysis, showing the algorithm's usefulness in electromagnetic optimization problems.

Linear antenna array synthesis using cat swarm optimization

A parallel genetic algorithm (GA) optimization tool has been developed for the synthesis of arbitrarily shaped beam coverage using planar 2D phased-array antennas. Typically, the synthesis of a contoured beam footprint using a planar 2D array is difficult because of the inherently large number of degrees of freedom involved (in general, the amplitude and phase of each element must be determined). We make use of a parallel GA tool in this study to compensate for this aspect of the design problem. The algorithm essentially compares a desired pattern envelope with that of trial arrays, and quantifies the effectiveness or desirability of each test case via a fitness function. The GA uses this information to rank and select subsequent arrays over a given number of generations via the conventional stochastic operators, i.e., selection, crossover, and mutation. Each fitness evaluation of a trial pattern is done on a node of the aerospace fellowship cluster supercomputer, which increases the speed of the algorithm linearly with the number of nodes. Because of the continuous nature of the parameters for this optimization problem, a real parameter encoding scheme is employed for the GA chromosome in order to avoid the quantization errors associated with a binary representation. A benchmark 10 times 10 (100) element array is employed, and various results of optimized coverage patterns are shown herein to illustrate the effectiveness and validity of the technique.

Parallel Genetic-Algorithm Optimization of Shaped Beam Coverage Areas Using Planar 2-D Phased Arrays

This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are "global" numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive at the final optimal solution. After providing a detailed explanation of how a genetic algorithm works, and a listing of a MATLAB code, the article presents three examples. These examples demonstrate how to optimize antenna patterns and backscattering radar-cross-section patterns. Finally, additional details about algorithm design are given. >

Introduction to Genetic Algorithms in Electromagnetics

A method is proposed for the synthesis of large planar non-regular arrays assuming constant magnitude for the excitation of the elements. The approach, which combines global and local optimization, is based on distinguishing aperture-type and non-coherent parts of the array factor. The following constraints are considered: minimal separation of the antennas, maximum size of the array and fixed mainbeam width. The level of the sidelobes is reduced via the minimization of a new type of flexible averaging cost function based on an Lp-norm. Possible applications of this model lie in the field of radio astronomy, satellite communications and radar systems. The proposed optimization strategy consists of three successive steps designed to be independent of each other. Starting with an equivalent continuous aperture, the first two steps act as global transformations of the radial and azimuthal positions of the elements in the initial array, while the third step performs a local optimization of individual elements of the array. This last step heavily relies on computations of the gradient of the cost function, which can be done quickly using an FFT-based procedure. The method is illustrated for several large-scale examples by considering as inputs four different types of non-regular arrays.

A Global-Local Synthesis Approach for Large Non-Regular Arrays

We propose a pattern-search-like algorithm to design an aperiodic optical phased array for extensive applications in light detection and ranging and free-space communication. The designed phased array with 128 isotropic elements achieves a scan range, peak side-lobe level, minimum beam width, and mean pitch of ± 82°, –14.34 dB, 0.062°, and 9.75 μm, respectively. To our knowledge, it has the widest steering range, narrowest divergence, and largest mean pitch for the same waveguide number. The minimum pitch can be greater than 2.67λ to avoid cross-coupling. The calculated relationship between the machine error and side-lobe level indicates that the designed structure has a higher error tolerance than its uniformly spaced counterpart.

Omnidirectional beam steering using aperiodic optical phased array with high error margin.

The sidelobe level (SLL) of linear and planar phased arrays is optimized using a genetic algorithm (GA). The amplitude and phase of the current of the antennas are all taken as variables to be controlled. As a final example, the design specifications for a 100 element linear array and a 100 element planar array are given. The linear and planar arrays are also optimized over scan angle. Simulation results are presented as several plots. Successful applications show that the GA can be used as a general tool for pattern synthesis of arbitrary arrays.

F. Soltankarimi

Papers

Side lobe level optimization in phased array antennas using genetic algorithm