Novel Nature-Derived Intelligent Algorithms and Their Applications in Antenna Optimization

Novel Nature-Derived Intelligent Algorithms and Their Applications in Antenna Optimization

Bo Xing (University of Limpopo, South Africa)
Copyright: © 2017 |Pages: 34
DOI: 10.4018/978-1-5225-1759-7.ch060

Abstract

With the rapidly developing of wireless communications, their adoption and utilization is increasing swiftly in various contexts. Among others, the issues relevant to antenna optimization are popularly known as the most important research subject for different wireless communications. Nowadays, a large number of studies have been published but spreading in a number of unrelated publishing directions which may hamper the use of such published resources. Furthermore, traditional approaches applied to this topic are normally based on simplified electromagnetic calculations which can only approximate real antenna performance. More recently, nature-inspired intelligent algorithms have become available to investigate antenna characteristics before construction. The advent of these algorithms has allowed different antenna design to be improved using mathematical optimization techniques. These provide us with the motivation of analyzing the existing studies in order to categorize and synthesize them in a meaningful manner.
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Background Of Novel Computational Intelligence

Computational intelligence (CI) is a fairly new research field, which is still in a process of evolution. At a more general level, CI comprises a set of computing systems with the ability to learn and deal with new events/situations, such that the systems are perceived to have one or more attributes of reason and intelligence (Marwala & Lagazio, 2011). According to their popularity, the CI methods can be roughly classified into two categories, namely, traditional CI and novel CI. Typically, traditional CI approaches include such as simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), Tabu search (TS), and artificial neural network (ANN). In terms of novel CI approaches (Xing & Gao, 2014), this overview covers the following candidates: artificial bee colony (ABC), biogeography-based optimization (BBO), bacterial foraging optimization (BFO), bee algorithm (BA), cat swarm optimization (CSO), central force optimization (CFO), cuckoo search (CS), electromagnetism-like (EM) algorithm, firefly algorithm (FA), harmony search (HS), invasive weed optimization (IWO), seeker optimization algorithm (SOA).

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