Optimizations of Patch Antenna Arrays Using Genetic Algorithms Supported by the Multilevel Fast Multipole Algorithm

We present optimizations of patch antenna arrays using genetic algorithms and highly accurate full-wave solutions of the corresponding radiation problems with the multilevel fast multipole algorithm (MLFMA). Arrays of finite extent are analyzed by using MLFMA, which accounts for all mutual couplings between array elements efficiently and accurately. Using the superposition principle, the number of solutions required for the optimization of an array is reduced to the number of array elements, without resorting to any periodicity and similarity assumptions. Based on numerical experiments, genetic optimizations are improved by considering alternative mutation, crossover, and elitism mechanisms. We show that the developed optimization environment based on genetic algorithms and MLFMA provides efficient and effective optimizations of antenna excitations, which cannot be obtained with array-factor approaches, even for relatively simple arrays with identical elements

Küçük dikey eksenli rüzgâr türbini için basit kontrol tasarımı (Simple control design for a small vertical axis wind turbine)

Bu makalede, küçük dikey eksenli rüzgâr türbinin elde ettiği enerjiyi maksimize edecek basit bir kontrolör tasarlanmıştır. Bu önerilen kontrol algoritmasının amacı mevcut sistemlere kıyasla daha basit bir yapıda olmasıdır. Algoritma kontrol işlemini sisteme uygulanan yük katsayısını önceden belirlenen değer aralıklarında müdahalede bulunarak yapabilmektedir. Bunu yapmak için önceden enerjiyi maksimize eden bir optimizasyon yöntemiyle belirlenmiş olan sınır değerlerinden faydalanmaktadır. Bu makalede, değişik simülasyonlar sonucu elde edilen enerjiyi maksimize ederken, basitleştirilmiş bir dikey eksenli rüzgâr türbini modeli kullanılmıştır

Optimizations of Patch Antenna Arrays Using Genetic Algorithms Supported by the Multilevel Fast Multipole Algorithm

We present optimizations of patch antenna arrays using genetic algorithms and highly accurate full-wave solutions of the corresponding radiation problems with the multilevel fast multipole algorithm (MLFMA). Arrays of finite extent are analyzed by using MLFMA, which accounts for all mutual couplings between array elements efficiently and accurately. Using the superposition principle, the number of solutions required for the optimization of an array is reduced to the number of array elements, without resorting to any periodicity and similarity assumptions. Based on numerical experiments, genetic optimizations are improved by considering alternative mutation, crossover, and elitism mechanisms. We show that the developed optimization environment based on genetic algorithms and MLFMA provides efficient and effective optimizations of antenna excitations, which cannot be obtained with array-factor approaches, even for relatively simple arrays with identical elements

Efficient Multilayer Iterative Solutions of Electromagnetic Problems Using Approximate Forms of the Multilevel Fast Multipole Algorithm

We consider efficient iterative solutions of large-scale electromagnetic problems involving metallic objects. For fast iterative solutions, a multilayer scheme using approximate forms of the multilevel fast multipole algorithm is developed. The approach is based on preconditioning each layer with iterative solutions at a lower layer, while the accuracy is changed from the top layer to the bottom layer. As opposed to the conventionally used algebraic preconditioners, the multilayer scheme: 1) does not require significant setup costs for large problems, and 2) does not require any additional memory. In addition, it can provide faster solutions, especially for large problems. The advantages of multilayer solutions are shown on canonical and complex geometries formulated with the combined field integral equation

Efficient Three-Layer Iterative Solutions of Electromagnetic Problems Using the Multilevel Fast Multipole Algorithm

We present a three-layer iterative algorithm for fast and efficient solutions of electromagnetic problems formulated with surface integral equations. The strategy is based on nested iterative solutions employing the multilevel fast multipole algorithm and its approximate forms. We show that the three-layer mechanism significantly reduces solution times, while it requires no additional memory as opposed to algebraic preconditioners. Numerical examples involving three-dimensional scattering problems are presented to demonstrate the effectiveness of the proposed algorithm

Dual-Band Antenna Array Optimizations Using Heuristic Algorithms and the Multilevel Fast Multipole Algorithm

We consider design and simulations of dual-band antenna arrays and their optimizations via heuristic algorithms, particularly, genetic algorithms (GAs) and particle swarm optimization (PSO) methods. As shown below, these arrays consist of patch antennas of different sizes, depending on the target frequencies. The resulting radiation problems are solved iteratively, where the matrix-vector multiplications are performed efficiently with the multilevel fast multipole algorithm (MLFMA). MLFMA allows for realistic simulations of antenna arrays of finite extent, without any periodicity and similarity assumptions, while including all mutual couplings between the antennas. This way, we obtain effective and realistic optimizations

Efficient and Accurate Electromagnetic Optimizations Based on Approximate Forms of the Multilevel Fast Multipole Algorithm

We present electromagnetic optimizations by heuristic algorithms supported by approximate forms of the multilevel fast multipole algorithm (MLFMA). Optimizations of complex structures, such as antennas, are performed by considering each trial as an electromagnetic problem that can be analyzed via MLFMA and its approximate forms. A dynamic accuracy control is utilized in order to increase the efficiency of optimizations. Specifically, in the proposed scheme, the accuracy is used as a parameter of the optimization. We show that the developed mechanism with dynamic accuracy control provides faster optimizations without deteriorating the quality of the final results in comparison to optimizations with full MLFMA

Multilayer Iterative Solutions of Large-Scale Electromagnetic Problems Using MLFMA

We present multilayer solutions of large-scale electromagnetic problems using the multilevel fast multipole algorithm (MLFMA). With the conventional algebraic preconditioners based on the available near-field interactions, the cost of iterative solutions may exceed the linearithmic complexity, particularly for ill-conditioned systems, despite the efficient matrix-vector multiplications by MLFMA. We show that, using a multilayer approach employing approximate and full versions of MLFMA, the complexity can be reduced to the desired levels without deteriorating the accuracy. The proposed approach significantly accelerates iterative solutions also for well-conditioned system, while it does not require any extra memory as opposed to memory-hungry algebraic preconditioners. Numerical results of scattering problems involving both canonical and complicated structures are presented to demonstrate the efficiency of the multilayer strategy

Antenna Switch Optimizations Using Genetic Algorithms Accelerated With the Multilevel Fast Multipole Algorithm

We present antenna switch optimizations using an efficient mechanism based on genetic algorithms and the multi-level fast multipole algorithm (MLFMA). Genetic algorithms are used to determine switch states for desired radiation and input characteristics, while cost-function evaluations are performed efficiently via an MLFMA implementation with dynamic error control. MLFMA is integrated into the genetic algorithm by extracting common computations to be performed once per optimization. Iterative convergence rates are further accelerated by using earlier solutions as initial-guess vectors. The efficiency of the developed mechanism is demonstrated on antennas with relatively large numbers of switches