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

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

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

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

Comparative and comprehensive study of linear antenna arrays’ synthesis

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm

Optimization of milling parameters using ant colony optimization

In process planning of conventional milling, selecting reasonable milling parameters is necessary to satisfy requirements involving machining economics, quality and safety. This study is to develop optimization procedures based on the Ant Colony Optimization (ACO). This method was demonstrated for the optimization of machining parameters for milling operation. The machining parameters in milling operations consist of cutting speed, feed rate and depth of cut. These machining parameters significantly impact on the cost, productivity and quality of machining parts. The developed strategy based on the maximize production rate criterion. This study describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. The ACO simulation is develop to achieve the objective to optimize milling parameters to maximize the production rate in milling operation. The Matlab software will be use to develop the ACO simulation. All the references are taken from related articles, journals and books. An example to apply the Ant Colony Algorithm to the problem has been presented at the end of the paper to give clear picture from the application of the system and its efficiency. The result obtained from this simulation will compare with another method like Genetic Algorithm (GA) and Linear Programming Technique (LPT) to validation. The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation