Design of a Novel Antenna Array Beamformer Using Neural Networks Trained by Modified Adaptive Dispersion Invasive Weed Optimization Based Data

A new antenna array beamformer based on neural networks (NNs) is presented. The NN training is performed by using optimized data sets extracted by a novel Invasive Weed Optimization (IWO) variant called Modified Adaptive Dispersion IWO (MADIWO). The trained NN is utilized as an adaptive beamformer that makes a uniform linear antenna array steer the main lobe towards a desired signal, place respective nulls towards several interference signals and suppress the side lobe level (SLL). Initially, the NN structure is selected by training several NNs of various structures using MADIWO based data and by making a comparison among the NNs in terms of training performance. The selected NN structure is then used to construct an adaptive beamformer, which is compared to MADIWO based and ADIWO based beamformers, regarding the SLL as well as the ability to properly steer the main lobe and the nulls. The comparison is made considering several sets of random cases with different numbers of interference signals and different power levels of additive zero-mean Gaussian noise. The comparative results exhibit the advantages of the proposed beamformer

Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling

Broadcasting antenna array null filling is a very challenging problem for antenna design optimization. This paper compares five antenna design optimization algorithms (Differential Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive Invasive Weed) as solutions to the antenna array null filling problem. The algorithms compared are evolutionary algorithms which use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. The focus of the comparison is given to the algorithm with the best results, nevertheless, it becomes obvious that the algorithm which produces the best fitness (Invasive Weed Optimization) requires very substantial computational resources due to its random search nature

Optimal Wideband LPDA Design for Efficient Multimedia Content Delivery over Emerging Mobile Computing Systems

An optimal synthesis of a wideband Log-Periodic Dipole Array (LPDA) is introduced in the present study. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio and the side lobe level, over a wide frequency range. The LPDA geometry that complies with the above requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called Invasive Weed Optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO method has been chosen to optimize an LPDA for operation in the frequency range 800-3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems

IWO-based Synthesis of Log-Periodic Dipole Array

The Invasive Weed Optimization (IWO) is an effective evolutionary and recently developed method. Due to its better performance in comparison to other well-known optimization methods, IWO has been chosen to solve many complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO is applied to optimize the geometry of a realistic log-periodic dipole array (LPDA) that operates in the frequency range 800-3300 MHz and therefore is suitable for signal reception from several RF services. The optimization is applied under specific requirements, concerning the standing wave ratio, the forward gain, the gain flatness and the side lobe level, over a wide frequency range. The optimization variables are the lengths and the radii of the dipoles, the distances between them, and the characteristic impedance of the transmission line that connects the dipoles. The optimized LPDA seems to be superior compared to the antenna derived from the practical design procedure

The Optimization of Energy Supply Systems by Sequential Streamflow Routing Method and Invasive Weed Optimization Algorithm; Case Study: Karun II Hydroelectric Power Plant

Among the major sources of energy supply systems, hydroelectric power plants are more common. Energy supply during peak hours and less environmental issues are some of the most important advantages of hydroelectric power plants. In this study, designing parameters to supply maximum amount of energy was determined by using the simulation-optimization perspective and combination of IWO-WEAP models. Subsequently, the developed model has been applied for designing the Karun II hydroelectric power plant. The sequential streamflow routing method has been developed for obtaining energy in WEAP water resources management software. In addition the optimization algorithm has been applied to optimize the invasive weeds. To verify the performance of this method, obtained results for the firm energy were compared to those of the total energy. Using this method, for 1398 GWY (Giga watt per your) firm energy, the minimum and normal levels of operation were 668 and 672 m.a.s.l (meters above sea level), respectively, and the installation capacity calculated around 498 MW as optimal value

An Improved Tabu Search Algorithm Based on Grid Search Used in the Antenna Parameters Optimization

In the mobile system covering big areas, many small cells are often used. And the base antenna’s azimuth angle, vertical down angle, and transmit power are the most important parameters to affect the coverage of an antenna. This paper makes mathematical model and analyzes different algorithm’s performance in model. Finally we propose an improved Tabu search algorithm based on grid search, to get the best parameters of antennas, which can maximize the coverage area and minimize the interference

Adaptive bio-inspired firefly and invasive weed algorithms for global optimisation with application to engineering problems

The focus of the research is to investigate and develop enhanced version of swarm intelligence firefly algorithm and ecology-based invasive weed algorithm to solve global optimisation problems and apply to practical engineering problems. The work presents two adaptive variants of firefly algorithm by introducing spread factor mechanism that exploits the fitness intensity during the search process. The spread factor mechanism is proposed to enhance the adaptive parameter terms of the firefly algorithm. The adaptive algorithms are formulated to avoid premature convergence and better optimum solution value. Two new adaptive variants of invasive weed algorithm are also developed seed spread factor mechanism introduced in the dispersal process of the algorithm. The working principles and structure of the adaptive firefly and invasive weed algorithms are described and discussed. Hybrid invasive weed-firefly algorithm and hybrid invasive weed-firefly algorithm with spread factor mechanism are also proposed. The new hybridization algorithms are developed by retaining their individual advantages to help overcome the shortcomings of the original algorithms. The performances of the proposed algorithms are investigated and assessed in single-objective, constrained and multi-objective optimisation problems. Well known benchmark functions as well as current CEC 2006 and CEC 2014 test functions are used in this research. A selection of performance measurement tools is also used to evaluate performances of the algorithms. The algorithms are further tested with practical engineering design problems and in modelling and control of dynamic systems. The systems considered comprise a twin rotor system, a single-link flexible manipulator system and assistive exoskeletons for upper and lower extremities. The performance results are evaluated in comparison to the original firefly and invasive weed algorithms. It is demonstrated that the proposed approaches are superior over the individual algorithms in terms of efficiency, convergence speed and quality of the optimal solution achieved