Abstract
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.
Highlights
Without some negative gravity these array configurations are overlooked. This Yagi-Uda array design example suggests that antennas optimized or designed using deterministic Central Force Optimization (CFO) may well benefit by including a small amount of negative gravity, and that the negative gravity approach merits further study
Many Global Search and Optimization (GSO) algorithms have been applied to the Yagi design problem against a variety of performance measures using various techniques, as examples: Gain, impedance, bandwidth using Particle Swarm Optimization (PSO) [8]; Yagi design using Multi-Objective Differential Evolution (DE) [9]; 6-Element array design based on Gain-Impedance Multiobjective Optimization [10]; Overview of Yagi design methods, many of them GSO’s [11]; Evolutionary methods for Yagi design [12]; Maximum gain optimization using Biogeography Based Optimization (BBO) variants [13]; Ultrawideband 6-element Yagi design using CFO/Variable Z0 [14]; 6-Element Yagi design using Dominating Cone Line Search (DCLS) [15]; Dipole array optimization using FitnessAdaptive dipole element (DE) [16]; Yagi design using Genetic Algorithms [17]; Wideband 6-element Yagi design using Adaptive DE [18]
This paper reports results for a typical 6-element Yagi design developed with a basic Central Force Optimization (CFO) implementation that includes a small amount of pseudo randomly injected negative gravity
Summary
The basic Yagi comprises a single driven dipole element (DE) flanked by a single parallel parasitic reflector (REF) on one side and any number of parasitic parallel directors on the other (Di). A typical six-element geometry is shown in Figure 1 (red dot marking DE feedpoint). The X-axis is the Yagi’s “boom”, and the array’s elements are an arranged along it in the X-Y plane parallel to each other and to the Y-axis as shown. REF, the element that is closest to the Y-axis, is closer to DE than the first director and longer than DE while the Di are shorter, but these characteristics do not by any means constitute absolute design requirements, as they are typical geometry that produces appropriate phase and current relationships between the array elements
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