Synthesis of concentric circular antenna arrays using dragonfly algorithm

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.

In this paper, the design of non-uniform concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level reduction is presented. The newly-proposed global evolutionary optimization methods; namely: the biogeography based optimization (BBO) is used to determine a set of weights that provides a radiation pattern with optimum side lobe level reduction with the constraint of a fixed major lobe beamwidth. The results obtained from BBO are compared with those obtained using the Matlab function Fmincon which uses a sequential quadratic programming (SQP) method. The comparison shows that the design of non-uniformly excited CCAAs using SQP method provides a side lobe level reduction that is better than that obtained using global stochastic optimization methods, indicating that global optimization techniques are not really needed in this problem.

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

A new approach for diminution of side lobe Ratio or level (SLL)maximum with half power beam width (HPBW) of thinned large multiple concentric circular antenna of the uniformly exuberated isotropic element, is proposed. Itis a novel optimization algorithm based on Seeker Optimization Algorithm (SOA) is used. Concentric Circular Antenna Array (CCAA) has been greatly recognized within subject of base radio station of the radio and telecommunications system. Since it has a 360-degree scan around its focus, beam pattern and all-azimuth scan capability, it has numerous advantages over other array geometries. A 9 ringed CCAA with a central element feeding is taken into consideration. CCAA which is having isotropic elements is examined by this algorithm and is shown appropriate.. Optimization efficiency is justified by antenna array synthesis, which is the result of themassive simulation. In all the cases mentioned in the paper, relative sidelobe level (SLL) is less than −20 dB with fixed HPBW compared to case of a uniform array. By using SOA with isotropic elements, the results of the simulation depict the amount of effective antenna elements can be pared from 279 to 120 with consistent abatement in side lobe ratio by 21.04 dB relative to that of the main beam with a fixed HPBW. Standard Particle Swarm Optimization (PSO) is used here to scrutinize outcome of SOA.

Predicting heat transfer rate of a ribbed triple-tube heat exchanger working with nanofluid using neural network enhanced by advanced optimization algorithms

This paper presents the implementation of the Genetic Algorithm to optimize the design of a large-scale sparse receiving array for innovative bistatic radar systems aimed at space debris detection. The optimization procedure is employed to find a preliminary configuration for a thinned array that exhibits maximum side lobe level suppression and simultaneously reduces the number of active elements. The results obtained through the exploited strategy are compared with those of the basic configuration with uniformly fed elements and those resulting from the use of tapering with traditional windowing techniques. The proposed thinned array configuration shows a peak directivity of 33.5 dBi and a Side Lobe Level (SLL) of -31.5 dB in the principal planes, with a 19 % reduction of the number of elements, meeting the specifications required by the application scenario.

A new method of sparse array synthesis based on quantum search algorithm is proposed. The method ensures the constraints include the number of elements, the array aperture and the minimum element spacing can still be satisfied by setting rational parameters of achimedean spiral reasonably when array elements are distributed along archimedean spiral. It turns the optimization problem of sparse plane array into the optimization of asymmetrical linear arrays, which will reduce the complexity of solving problems greatly. The quantum search algorithm (QSA) is a new global optimization methods, which is inspired by quantum mechanics theories, and has excellent global optimization performance. Nowadays, the application of QSA in sparse plane arrays synthesis is few. The method combines archimedean spiral and the QSA, make the sparse plane array produce lower peak sidelobe level (PSLL). The simulated results show that the approach is high efficiency and robust, and can apply to problem of sparse plane antenna array synthesis with the constraint of minimum element spacing and aperture.

Micropropagation techniques offer opportunity to proliferate, maintain, and study dynamic plant responses in highly controlled environments without confounding external influences, forming the basis for many biotechnological applications. With medicinal and recreational interests for Cannabis sativa L. growing, research related to the optimization of in vitro practices is needed to improve current methods while boosting our understanding of the underlying physiological processes. Unfortunately, due to the exorbitantly large array of factors influencing tissue culture, existing approaches to optimize in vitro methods are tedious and time-consuming. Therefore, there is great potential to use new computational methodologies for analyzing data to develop improved protocols more efficiently. Here, we first tested the effects of light qualities using assorted combinations of Red, Blue, Far Red, and White spanning 0–100 μmol/m2/s in combination with sucrose concentrations ranging from 1 to 6% (w/v), totaling 66 treatments, on in vitro shoot growth, root development, number of nodes, shoot emergence, and canopy surface area. Collected data were then assessed using multilayer perceptron (MLP), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) to model and predict in vitro Cannabis growth and development. Based on the results, GRNN had better performance than MLP or ANFIS and was consequently selected to link different optimization algorithms [genetic algorithm (GA), biogeography-based optimization (BBO), interior search algorithm (ISA), and symbiotic organisms search (SOS)] for prediction of optimal light levels (quality/intensity) and sucrose concentration for various applications. Predictions of in vitro conditions to refine growth responses were subsequently tested in a validation experiment and data showed no significant differences between predicted optimized values and observed data. Thus, this study demonstrates the potential of machine learning and optimization algorithms to predict the most favorable light combinations and sucrose levels to elicit specific developmental responses. Based on these, recommendations of light and carbohydrate levels to promote specific developmental outcomes for in vitro Cannabis are suggested. Ultimately, this work showcases the importance of light quality and carbohydrate supply in directing plant development as well as the power of machine learning approaches to investigate complex interactions in plant tissue culture.

Hybrid weighted probabilistic neural network and biogeography based optimization for dynamic economic dispatch of integrated multiple-fuel and wind power plants

This paper illustrates an application of adaptive tabu search (ATS) to optimal power flow (OPF) problems in comparison with some effective mathematical and evolutionaryoptimization methods. Although, the ATS was originally developed for solving a combinatorial optimization problem whose parameters are discrete, it has the ability to handle continuous variables by treating them as discrete ones with a very small variable step-size to gain accuracy. The proposed algorithm was tested with 9-bus and 300-bustest systems to represent a small-scale and a comparatively large-scale power system, respectively. Each test power system was challenged by performing three test cases. The first test case was given by applying a quadratic function to generators’ fuel-cost curve, whereas a non-smooth fuel-cost function was assigned to the second. In addition, the system voltage profile was considered and set as the objective function to be minimized in the last test case. The comparisons among solutions obtained by sequential quadratic programming (SQP), evolutionary programming (EP) and the ATS were carried out, from which satisfactory results and the selection of solution methods to OPF problems were summarized. Key words: Optimal power flow problem, sequential quadratic programming, evolutionary programming, adaptive tabu search, quadratic fuel cost, non-smooth fuel cost.

Enhancement of performance of antenna array in terms of directive characteristics requires simultaneous control of both side-lobe level (SLL) and beam width (BW). The population-based evolutionary computing techniques are best suited for such array synthesis problems. In this paper, a novel evolutionary computing tool known as flower pollination algorithm (FPA) is applied to linear array synthesis problem. In addition, the performance of both amplitude only and amplitude–spacing-based methods is assessed. The proposed method estimates the weights of each objective so that the generated radiation patterns with desired SLL and BW are produced. The performance and the efficiency of FPA-based method are evaluated and also compared with those obtained using genetic algorithm and particle swarm optimization. The problem of synthesizing patterns with narrow and wide nulls is also investigated. Multiple narrow band nulls as well wide nulls are produced using the FPA with amplitude only method. The investigation has been made for both sum and difference patterns.

In certain antenna arrays (“switched‐element” arrays), some of the radiating elements are turned on or off during operation to provide some desired reconfigurability at different times, or are effectively turned on or off at different frequencies, through the use of filters connected to groups of radiating elements, in order to retain the same performance over wide‐frequency bands. In such circumstances, the excitations of the elements must be the same, irrespective of whether all or only some of the elements are being used. Thus, one may wish to synthesize an array (that is, determine its element excitations) so that the required performance is obtained under different sets of circumstances. In this paper, we indicate how the use of a parallel approach allows the method of generalized projections to be used for such synthesis problems, and demonstrate its use for a specific example. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 40: 465–471, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20006

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.

Beam synthesis remains one of the most difficult problems for an antenna array designer, this task being made more difficult when one limits the number of elements to a handful. Pattern synthesis in planar arrays is often achieved by the sampling of a continuous aperture distribution at the element locations. In the case of arrays of limited numbers of elements this produces errors that can unacceptably distort the radiation pattern. When one also then considers the effects of mutual coupling between the antenna elements, the application of ideal element weights derived in this way are no longer valid. In the paper a technique is presented, using the concept of retrodirective beams, that can achieve a low sidelobe beam from a continuous distribution on a circular aperture. The theory is then extended to include a planar array of antenna elements. This is achieved by the addition of retrodirective beams to the uniform beam pattern of the array. Measured data is presented for a small planar array antenna using the derived method. A method for correction of the element weights, to minimise the effects of mutual coupling, producing low sidelobe radiation patterns is then applied. No knowledge of the antenna array's coupling coefficients are required.

We propose a new boundary condition for the particle swarm optimization (PSO) method. The new boundary is duped periodic boundary condition (PBC) and is inspired by a molecular dynamics (MD) formulation of the PSO. Based on this formulation, the PBC is introduced formally by postulating that the swarm environment is similar to crystal-bulk materials studied using MD. A combination of soft and hard boundary modes is employed in the implementation of the hybrid version of the proposed PBC. The hybrid PBC constitutes a substantial alteration of the original scenario assumed in theoretical physics but is allowed here to suite the PSO environment. When applied to standard test functions and the practical problem of linear antenna array synthesis, the hybrid PBC proved to be superior to previously established boundary conditions.