Array pattern optimization for a steerable circular isotropic antenna array using the firefly algorithm

An improved procedure to lessen side lobe levels (SLLs) and side band levels (SBLs) in time modulated linear antenna arrays (TMLAAs) by using the optimization process. In that optimization process we give the discrete values of static excitation amplitudes and phase of continuous values of on-time duration of the elements of antenna array. We used the particle swarm optimization (PSO) technique to reduce the maximum of SLLs and SBLs to a pre defined desired value of antenna element. This has been optimized in a way that the discrete values of input excitation amplitude are obtained by PSO algorithm. The simulation result of time-modulated linear antenna array (TMLAA) is presented by reducing SLL and SBLmax to below −40 dB and to −35 dB respectively by taking 30 elements.

Non-uniform Concentric Circular Antenna Array Design using IPSO Technique for Side Lobe Reduction

In this paper, the novel hybrid enhanced particle swarm optimization and differential evolution (EPSO/DE) optimization technique [1–2] is used in designing different configurations of concentric circular arrays (CCAA) and concentric hexagonal arrays (CHAA). Many advantages were achieved by the rearrangement of the circular arrays (CA) and hexagonal arrays (HA) elements into CCAA and CHAA multi-ring geometries. Different configurations with two-rings, three-rings and four-rings are designed with the same number of elements and the same main beamwidth of the initial circular and helical geometries. The objective of this paper is to design optimum multi-ring antenna arrays that achieve minimum sidelobe level (SLL), high directivity and reduced mutual coupling in compare to traditional circular and helical arrays. Hybrid EPSO/DE algorithm is applied to optimize the complex weights (amplitude and phase) of the elements current. The objective is to minimize the SLL of the concentric configuration arrays while achieving the highest possible directivity and minimum mutual coupling.

In this paper the optimal performance of time modulated nine-ring concentric circular antenna array with isotropic elements has been studied based on an evolutionary optimization algorithm hybridized with local heuristic search called memetic firefly algorithm (MFA). The firefly algorithm has been applied followed by Nelder–Mead simplex method for the local heuristic search to achieve the optimal fine tuning. Other algorithms like real coded genetic algorithm (RGA) and particle swarm optimization (PSO) have been used for the comparison purpose. The comparisons among the algorithms have been made with two case studies as Case-1 and Case-2, and with two different fitness functions $$(f_{{ fitness}1}, f_{{ fitness}2})$$ and three control parameters like inter-element uniform/non-uniform spacing in rings, inter-ring radii and the switching-on times of rings. The simulation results show that the MFA outperforms RGA and PSO for both the cases Case-1, Case-2 and $$f_{{ fitness}1}$$ , $$f_{{ fitness}2}$$ , respectively with respect to better side lobe level (SLL). The fitness function $$f_{{ fitness}2}$$ is better than the $$f_{{ fitness}1}$$ with respect to sideband level. Apart from this, powers radiated at the centre/fundamental frequency and the first two sideband frequencies, and dynamic efficiency have been computed. It is found that power radiated by any sideband frequency is much less as compared to the power radiated at the centre frequency. It has been observed that as the sideband frequency increases, SBL decreases to the greater extent as compared to SLL. As per authors’ knowledge there is a little research contribution by any other previous researcher regarding numerical computation of radiation characteristics as SBLs, powers radiated at the fundamental frequency and its two sideband frequencies, directivity, and dynamic efficiency for time-modulated CCAA.

In this paper synthesis of single ring non-uniform circular antenna for maximum side lobe level (SLL) reduction and improved first null beamwidth (FNBW) is dealt with. Evolution based Differential Evolution (DE) is used to determine an optimal set of current excitation weights and antenna inter-element spacing that provides optimal radiation pattern. Circular array antenna laying on x-y plane is assumed. DE is applied on circular arrays of 8-, 10-, and 12- elements. Various simulation results are presented and hence performances of SLL and FNBW are analyzed. Experimental results show considerable reductions of both the SLL and FNBW with respect to those of the uniform case and some standard algorithms GA [12], PSO [14] and SA [11] applied to the same problem of non-uniform circular antenna arrays.

Evolutionary optimization techniques with multiple objectives are applied to the design of microstrip antennas. The biologically inspired algorithms, Particle Swarm Optimization, Genetic Algorithms and the Firefly Algorithm, are integrated in new software, Antenna Optimizer, which combines attributes of the electromagnetic design environment of CST Microwave Studio with those of the technical computing and programming environment of MATLAB. Impedance matching and gain improvement are optimized over a predefined frequency range, resulting in a very small and compact 12 mm × 21 mm ultra-wideband antenna which was fabricated and measured.

This paper investigates the reduction of side lobe level in antenna arrays. Reduction of side lobe level in antenna arrays has some limitations including fixed width of the beam. We have modeled the side lobe level reduction as an optimization problem using simulated annealing technique for side lobe level reduction of a specific beam width. The advantage of this method compared with other methods is that it can get out of local minimums and converge to the optimized answer. Efficiency of simulated annealing algorithm in pattern extraction of desired circular antenna, which is used frequently in modern telecommunication and radar systems, is investigated and the results are compared with that of genetic and evolutionary algorithms. Key words: Simulated annealing, antenna array, circular array, non-uniform antenna array, side lobe level.

This research has proposed the iterative genetic algorithm (GA) optimization scheme to synthesize the radiation pattern of an aperiodic (nonuniform) linear array antenna. The aim of the iterative optimization is to achieve a radiation pattern with a side lobe level (SLL) of ≤−20 dB. In the optimization, the proposed scheme iteratively optimizes the array range (spacing) and the number of array elements, whereby the array element with the lowest absolute complex weight coefficient is first removed and then the second lowest and so on. The removal (the element reduction) is terminated once the SLL is greater than −20 dB (>−20 dB) and the elemental increment mechanism is triggered. The results indicate that the proposed iterative GA optimization scheme is applicable to the nonuniform linear array antenna and also is capable of synthesizing the radiation pattern with SLL ≤ −20 dB.

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array’s element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms.

In this paper the maximum sidelobe level (SLL) reductions without and with central element feeding in various designs of three-ring concentric circular antenna arrays (CCAA) are examined using a novel particle swarm optimization algorithm (NPSO) to finally determine the global optimal CCAA design. Real coded Genetic Algorithm (RGA) is also employed for comparative optimization but it proves to be suboptimal. The present paper assumes non-uniform excitations and uniform spacing of excitation elements in each three-ring CCAA design. Among the various CCAA designs, the three-ring CCAA containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design with global minimum SLL (-19.69dB) determined by NPSO.

This paper investigates the use of a Hybrid Energy Storage System (HESS) that contains a battery and fuel cell system with a microgrid (MG) to perform multiple functions such as supply-demand matching and energy arbitrage. The main objective of the proposed study is to obtain the optimal size of the MG system which minimizes the total costs and the total emissions. The problem is formulated as a nonlinear optimization problem with different constraints and solved using Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm. To validate the effectiveness of the HFPSO, the HFPSO is compared with the Particle Swarm Optimization (PSO) algorithm and the Firefly algorithm (FA). The result of the comparison proves the effectiveness of the HFPSO, and this is considered the main reason for using the HFPSO to solve the proposed difficult optimization problem. The problem is implemented using MATLAB software. The policy of the MG system is illustrated, and the results and the curves are discussed to validate this policy.

In this study, solar cell parameters, the most basic material of solar energy that has become increasingly widespread in recent years, are determined by Particle Swarm Optimization and Firefly and algorithms. The previously proposed single-diode solar cell model was used as a base for solar cells used in series connection and then in parallel connection with each other. Integral Absolute Error (IAE) was chosen as error criterion in heuristic algorithm for the parameters of a single diode solar cell. With this work, the parameters of the solar cell are brought closer to the reality, which is important because it will change the maximum power and efficiency to be drawn from the panes by designing a more accurate controller. In this study, it has been seen that the firefly algorithm gives more successful results when firefly and particle swarm optimization algorithms compared to the previous studies.

This work is concerned with the methodology for improvement of the radiation pattern performance and eliminating the effect of unwanted interference of symmetric time modulated linear antenna array (STMLAA) in the predefined direction. This is achieved using evolutionary technique called Novel Particle Swarm Optimization with Wavelet Mutation (NPSOWM) for various application specific optimization like SLL only reduction, dual broad null and multiple broad null optimization. The optimized results obtained by using NPSOWM are validated by CST-MWS by captivating practical elements of dipole antenna and its array configuration. The numerical results show the effect of the particular (practical) antenna on the array. Various examples have taken for this purpose. A set of 16-element antenna array have designed for SLL only reduction. 16-, 32-, and 64-element STMLAA for incorporating the dual broad nulls in the angular range from 400 to 700 over the elevation plane. A set of 24-, 30-, and 32-element antenna array have occupied for multiple broad nulls. The strength of the adopted algorithm is tested for different number of array antenna configuration, which establish the efficacy of the optimization technique NPSOWM adopted for achieving the interference cancellation in the predefined direction and performance improvement of the antenna array such as simultaneous improvements of broad nulls (BNs), sidelobe level (SLL), sideband level (SBL), and the directivity. From the simulated results it is clear that NPSOWM can perform better than that of un-optimized linear antenna array with respect to BNs, SLL, and SBLs and Directivity.

The field of circular (or cylindrical) arrays has received far less attention than that devoted to linear and planar arrays. At first sight this seems surprising since one of their principal advantages is the ability to deflect beams electronically through 360° with little change of either beamwidth or sidelobe level. Circular arrays have been applied, to a limited extent, in both communication systems and navigational aids since the late 1930s. There has, however, been surprisingly little change in their scale of application since that date, though there are now signs of a significant increase of interest in such arrays for applications to DF (Direction Finding) and ESM (Electronic Support Measures) systems. The analysis and general understanding of the properties of circular arrays has progressed steadily for many years and the most significant developments were probably made by several groups of workers in the 1960s when the concept of phase mode excitation was developed. The lack of applications in the past is prob ably due to the fact that the basic problems of exciting circular arrays with the correct values of amplitude and phase are, in general, more complex than for linear arrays. Furthermore, electronic scanning of directional patterns for circular arrays may be difficult to implement and can require both the amplitude and phase of each element of the array to be changed.

Hybrid firefly and Particle Swarm Optimization algorithm for the detection of Bundle Branch Block