Low side lobe pattern synthesis using projection method with genetic algorithm for truncated cone conformal phased arrays

A hybrid method for synthesizing antenna＇s three dimensional （3D） pattern is proposed to obtain the low sidelobe feature of truncated cone conformal phased arrays. In this method, the elements of truncated cone conformal phased arrays are projected to the tangent plane in one generatrix of the truncated cone. Then two dimensional （2D） Chebyshev amplitude distribution optimization is respectively used in two mutual vertical directions of the tangent plane. According to the location of the elements, the excitation current amplitude distribution of each element on the conformal structure is derived reversely, then the excitation current amplitude is further optimized by using the genetic algorithm （GA）. A truncated cone problem with 8x8 elements on it, and a 3D pattern desired side lobe level （SLL） up to 35 dB, is studied. By using the hybrid method, the optimal goal is accomplished with acceptable CPU time, which indicates that this hybrid method for the low sidelobe synthesis is feasible.

Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document.In this study,a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns.One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods.In this study,attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision,continuity and consistency.To investigate the efficiency of the proposed algorithm,the results of the study were evaluated with respect to the precision and recall criteria.The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.

Beam Pattern Synthesis Based on Hybrid Optimization Algorithm

As conventional methods for beam pattern synthesis can not always obtain the desired optimum pattern for the arbitrary underwater acoustic sensor arrays,a hybrid numerical synthesis method based on adaptive principle and genetic algorithm was presented in this paper.First,based on the adaptive theory,a given array was supposed as an adaptive array and its sidelobes were reduced by assigning a number of interference signals in the sidelobe region.An initial beam pattern was obtained after several iterations and adjustments of the interference intensity,and based on its parameters,a desired pattern was created.Then,an objective function based on the difference between the designed and desired patterns can be constructed.The pattern can be optimized by using the genetic algorithm to minimize the objective function.A design example for a double-circular array demonstrates the effectiveness of this method.Compared with the approaches existing before,the proposed method can reduce the sidelobe effectively and achieve less synthesis magnitude error in the mainlobe.The method can search for optimum attainable pattern for the specific elements if the desired pattern can not be found.

Optimization of Linear Antenna Arrays Based on Genetic Algorithms

The methods of moment and genetic algorithm (GA) are combined to optimize the Yagi Uda antenna array and Log periodic dipole antenna (LPDA) array. The element lengths and spacing are optimized for the Yagi Uda array; while the ratio factor of spacing to length as well as the ratio of length to diameter of the elements are optimized for LPDA array. The results show that the main parameters, such as gain and pattern, have been improved apparently; and the high back lobe level of LPDA can be reduced greatly, therefore, GA is a very competent method for optimizing the linear array as well as in other fields.

Sparse Planar Retrodirective Antenna Array Using Improved Adaptive Genetic Algorithm

An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach. This algorithm has been validated to be superior to the simple genetic algorithm （SGA） by a complicated binary testing function. Then the proposed algorithm is applied to optimizing the planar retrodirective array to reduce the cost of the hardware. The fitness function is discussed in the optimization example. After optimization, the sparse planar retrodirective antenna array keeps excellent retrodirectivity, while the array architecture has been simplified by 34%. The optimized antenna array can replace uniform full array effectively. Results show that this work will gain more engineering benefits in practice.

Pattern Synthesis and Polarization Optimization of a Conical Array

A feasible method for synthesizing millimeter-wave conical array and optimizing low cross-polarization is proposed.Starting from the far-field superposition principle,an efficient approach including element mutual coupling and mounted platform effects is used to calculate the far-field patterns.A coordinate transform is applied to create polarization quantities,and a general process for the element polarized pattern transformation is performed.Corresponding numerical example is given and the desired sidelobe level and low cross-polarization are optimized.The numerical results indicate the proposed method is valid.

Array Antenna Pattern Synthesis Based on Selective Levy Flight Culture Wolf Pack Algorithm

Due to the shortcomings such as the premature convergence and the bad local optimal searching capability in traditional intelligence methods for pattern synthesis,a new type of wolf pack algorithm named Levy⁃Cultural Wolf Pack Algorithm(LCWPA)was designed on the basis of the Cultural Wolf Pack Algorithm(CWPA),which obeys the selective Levy flight.Because of the good overall management ability provided by the cultural algorithm in optimization process and the characteristics of excellent population diversity brought by Levy flight,the search efficiency of the new algorithm was greatly improved.When the algorithm was applied in the pattern synthesis of array antenna,the simulation results showed its high performance with multi⁃null and low side⁃lobe restrictions.In addition,the algorithm was superior to the Quantum Particle Swarm Optimization(QPSO),Particle Swarm Optimization(PSO),and Genetic Algorithm(GA)in optimization accuracy and operation speed,and is of very good generalization.

Six-Element Yagi Array Designs Using Central Force Optimization with Pseudo Random Negative Gravity

A six-element Yagi-Uda array is optimally designed using Central Force Optimization (CFO) with a small amount of pseudo randomly injected negative gravity. CFO is a simple, deterministic metaheuristic analogizing gravitational kinematics (motion of masses under the influence of gravity). It has been very effective in addressing a wide range of antenna and other problems and normally employs only positive gravity. With positive gravity the six element CFO-designed Yagi array described here exhibits excellent performance with respect to the objectives of impedance bandwidth and forward gain. This paper addresses the question of what happens when a small amount of negative gravity is injected into the CFO algorithm. Does doing so have any effect, beneficial, negative or neutral? In this particular case negative gravity improves CFO’s exploration and creates a region of optimality containing many designs that perform about as well as or better than the array discovered with only positive gravity. Without some negative gravity these array configurations are overlooked. This Yagi-Uda array design example suggests that antennas optimized or designed using deterministic CFO may well benefit by including a small amount of negative gravity, and that the negative gravity approach merits further study.

Pattern synthesis optimization of 3-D ODAR based on improved GA using LSFE method

Pattern synthesis in 3-D opportunistic digital array radar(ODAR) becomes complex when a multitude of antennas are considered to be randomly distributed in a three dimensional space.In order to obtain an optimal pattern,several freedoms must be constrained.A new pattern synthesis approach based on the improved genetic algorithm(GA) using the least square fitness estimation(LSFE) method is proposed.Parameters optimized by this method include antenna locations,stimulus states and phase weights.The new algorithm demonstrates that the fitness variation tendency of GA can be effectively predicted after several "eras" by the LSFE method.It is shown that by comparing the variation of LSFE curve slope,the GA operator can be adaptively modified to avoid premature convergence of the algorithm.The validity of the algorithm is verified using computer implementation.

Optimizing bus services with variable directional and temporal demand using genetic algorithm

As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services.

A DERIVATIVE-FREE ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm,but in the search step of pattern search algorithm,the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication,crossover and mutation, a finite set of points can be used. In theory,the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems ,which other pattern search algorithms don＇t bear.

Directional Modulation Based on a Quantum Genetic Algorithm for a Multiple-Reflection Model

Directional modulation is one of the hot topics in data security researches.To fulfill the requirements of communication security in wireless environment with multiple paths,this study takes into account the factors of reflections and antenna radiation pattern for directional modulation.Unlike other previous works,a novel multiple-reflection model,which is more realistic and complex than simplified two-ray reflection models,is proposed based on two reflectors.Another focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna array.The quantum approach has strengths in convergence speed and the globe searching ability for the complicated model with the large-size antenna array and multiple paths.From this,a phased directional modulation transmission system can be optimized as regards communication safety and improve performance based on the constraint of the pattern of the antenna array.Our work can spur applications of the quantum evolutionary algorithm in directional modulation technology,which is also studied.

Multi-Step Amplitude Quantization for Ultralow Sidelobe Phased Arrays by Direct Optimization Synthesis

In this paper, a new amplitude quantization synthesis method for ultralow sidelobe phased arrays is proposed, which is based on the constrained nonlinear optimization algorithm. By introducing a set of critical constraint conditions into the optimization model, we can directly quantize the amplitude distribution instead of replacing it with a continuous equivalent aperture antenna. The mutual coupling and the element patterns are also considered in the quantization synthesis. Finally, some array simulation results are given to show the effectiveness of the method.

GA-based approach to phase compensation of large phased array antennas

The investigation of the effect of electrical and mechanical errors on the performance of a large active phased array antenna is studied. These errors can decrease the antenna performance, for instance, the gain reduction, side lobe level enhancement, and incorrect beam direction. In order to improve the performance of the antenna in the presence of these errors, phase error correction of large phased array antennas using the genetic algorithm（GA） is implemented. By using the phase compensation method, the antenna overall radiation pattern is recovered close to the ideal radiation pattern without error. By applying the simulation data to a 32×40 array of elements with a square grid at the frequency of S-band and measurement of the radiation pattern, the effectiveness of the proposed method is verified.

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design（CMSOD） belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search（ACAM） is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm（GA） and particle swarm optimization（PSO） are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.

Assessment of Different Optimization Algorithms for a Thermal Conduction Problem

In this study,three computational approaches for the optimization of a thermal conduction problem are critically compared.These include a Direct Method(DM),a Genetic Algorithm(GA),and a Pattern Search(PS)technique.The optimization aims to minimize the maximum temperature of a hot medium(a medium with uniform heat generation)using a constant amount of high conductivity materials(playing the role of fixed factor constraining the considered problem).The principal goal of this paper is to determine the most efficient and fastest option among the considered ones.It is shown that the examined three methods approximately lead to the same result in terms of maximum tem-perature.However,when the number of optimization variables is low,the DM is the fastest one.An increment in the complexity of the design and the number of degrees of freedom(DOF)can make the DM impractical.Results also show that the PS algorithm becomes faster than the GA as the number of variables for the optimization rises.

Antenna Array Pattern Synthesis via Coordinate Descent Method

This paper presents an array pattern synthesis algorithm for arbitrary arrays based on coordinate descent method (CDM). With this algorithm, the complex element weights are found to minimize a weighted L2 norm of the difference between desired and achieved pattern. Compared with traditional optimization techniques, CDM is easy to implement and efficient to reach the optimum solutions. Main advantage is the flexibility. CDM is suitable for linear and planar array with arbitrary array elements on arbitrary positions. With this method, we can configure arbitrary beam pattern, which gives it the ability to solve variety of beam forming problem, e.g. focused beam, shaped beam, nulls at arbitrary direction and with arbitrary beam width. CDM is applicable for phase-only and amplitude-only arrays as well, and furthermore, it is a suitable method to treat the problem of array with element failures.

Expansion of a Y-Shaped Antenna Array and Optimization of the Future Antenna Array in Malaysia for Astronomical Applications

To achieve high quality images from the sky by extending an existing interferometric array, in this work, the Geometrical Method (GM), Genetic Algorithm (GA), and Division Algorithm (DA) are compared. These methods are each applied independently to an interferometer array starting from the same initial conditions. Using the GM method, the spiral configuration is suggested as an optimum arrangement that provides the desired u-v coverage with low side lobe levels (SLLs). Using the GA method, as the number of generations is increased, the unsampled cells are reduced, enhancing the imaging quality. As such, the algorithm improves the overlapped samples as it works with a greater number of generations. Moreover, the GA is able to suppress the SLL. Finally, the DA is applied to such an array. Results show that the DA is able to process the sampled data with less overlapping of the data in the snapshot observations, in comparison to the other discussed configurations in this paper;effectively the DA reduces the overlapped samples, such that it is more efficient than the GA. The configuration of antennas that arrives by applying the DA method can achieve a certain image quality with less overlapping, as compared to the configuration arriving by applying the GA method. The calculated SLLs for the DA configuration are used to demonstrate that the efficiency of the DA is potentially better than that of the GA. Moreover, the GA and DA algorithms discussed in this study are applied to an array of 10 antennas with coordinates that represent the antennas deployed in Malaysia. Results show that the DA can reduce the overlapping of the samples more efficiently than the GA for a 6-hour tracking observation and in terms of unsampled cells the DA has the same efficiency of the GA.

A NOVEL HYBRID GENETIC ALGORITHMS AND PATTERN SEARCH TECHNIQUES FOR INDUSTRIAL PRODUCTION PLANNING

Soft computing has attracted many research scientists,decision makers and practicing researchers in recent years as powerful computational intelligent techniques,for solving unlimited number of complex real-world problems particularly related to research area of optimization.Under the uncertain and turbulence environment,classical and traditional approaches are unable to obtain a complete solution with satisfaction for the real-world problems on optimization.Therefore,new global optimization methods are required to handle these issues seriously.One such method is hybrid Genetic algorithms and Pattern search,a generic,flexible,robust,and versatile framework for solving complex problems of global optimization and search in real-world applications.

Pattern synthesis of antennas based on a modified particle swarm optimization algorithm

In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as global best perturbation and inertia weight jump threshold are adopted.The convergence speed and accuracy of the algo-rithm are improved.The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance.Thereafter,the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented.