A jumping genes multiobjective algorithm for antenna designs

Abstract

The design of the microwave antenna is traditionally conducted through trial and error method until an acceptable configuration meets the design requirements. It relies heavily on the ability and experience of the design specialists. As the requirements for antenna design are getting more and more complicated in nature, this way of antenna design become inefficient and sometime impossible particularly when the antenna structure is complex and comprises of abundant number of parameters. The adoption of computational aided design scheme is a natural approach for optimizing and tuning the antenna parameters. This thesis investigates a powerful computational optimization algorithm, which is known as Jumping Genes Evolutionary Algorithm (JGEA) for antenna design. It shares most of the structure of Genetic Algorithm (GA) and comprises only a simple operation that mimics the jumping gene phenomenon that was firstly discovered by the Nobel laureate, Barbara McClintock, from her work on corn plant. The suitability of JGEA for antenna design has been examined and a number of various types of antennas have been designed using this optimization technique. These include circular polarized antenna, wideband antenna, multi-band antenna, as well as ultra-wideband antenna. These antennas were hardware fabricated and verified by experimental measurements. Since the multi-objective optimization process generates many non-dominated solutions which has the trade-off among the design objectives, a multiple criteria decision making method is adapted to select the most suitable solution according to the preference of the decision makers. For further investigation on the optimization performance of JGEA, its performance in convergence and diversity has been evaluated and compared with other two heuristics methods such as Particle Swarm Optimization (PSO) and Simulated Annealing (SA). These results reinforced the notion of a good candidate for antenna design using the proposed the JGEA methodology.