Ant Colony Optimisation-based radiation pattern manipulation algorithm for Electronically Steerable Array Radiator Antennas

Abstract

A new algorithm for manipulating the radiation pattern of Electronically Steerable Array Radiator Antennas is proposed. A continuous implementation of the Ant Colony Optimisation (ACO) technique calculates the optimal impedance values of reactances loading different parasitic radiators placed in a circle around a centre antenna. By proposing a method to obtain a suitable sampling frequency of the radiation pattern for use in the optimisation algorithm and by transforming the reactance search space into the search space of associated phases, special care was taken to create a fast and reliable implementation, resulting in an approach that is suitable for real-time implementation. The authors compare their approach to analytical techniques and optimisation algorithms for calculating these reactances. Results show that the method is able to calculate near-optimal solutions for gain optimisation and side lobe reduction.

An improved quantum based particle swarm optimizer applied to electromagnetic optimization problems

“…Because deterministic approaches are not capable of determining the global solutions for these types of problems, much efforts have been devoted to the study of heuristic and stochastic methods in the past few decades. In this regard, many stochastic methods and their variants have been developed including, among others, the Tabu search method (Glover, 1989), ant colony algorithms (Dorigo, 1992), simulated annealing (Kirkpatrick et al, 1983), genetic algorithms (Schmitt, 2001), evolutionary algorithms (Deb, 2001), a fast and elitist multi-objective genetic algorithm: NSGA-II (Deb et al, 2002), ant colony optimizationbased radiation pattern manipulation algorithm (Aelterman et al, 2009), orthogonal methods based ant colony search (Hu et al, 2008), multi-objective optimization approaches (Carcangiu et al, 2007) and a benchmark problem (Barba et al, 2015) have all been proposed and used to solve electromagnetic optimization problems. Nevertheless, according to no free lunch theorem, there is no universal optimizer that can solve all optimization problems.…”

Antenna design exploiting adjoint sensitivity‐based geometry evolution

“…Global optimisation approaches may help find the global solution at the cost of a dramatic increase in the number of required EM simulations. These algorithms include simulated annealing [1], genetic algorithms [2], particle swarm optimisation [3], ant colony [4] and invasive weed optimisation [5]. All these techniques utilise nature-inspired mechanisms to locate the global optimal design.…”