Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution Algorithms

Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution Algorithms

Sotirios K. Goudos (Aristotle University of Thessaloniki, Greece)
Copyright: © 2018 |Pages: 14
DOI: 10.4018/978-1-5225-2255-3.ch572

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Differential evolution was introduced proposed by KennethV. Price and R. Storn in 1995. It uses real operators for mutation and crossover, instead of the binary operators used in the first GAs. That fact has made DE suitable for solving real-valued problems. DE is a very simple but very powerful stochastic global optimizer. It has been used to solve problems in many scientific and engineering fields and proved to be a very efficient and robust technique for global optimization. In 1997, Storn established a website (Rainer Storn) to where DE source code is publically available for several popular programming languages. Since then there is an explosive growth in differential evolution research.

One of the DE advantages is that very few control parameters have to be adjusted in each algorithm run. However, the control parameters involved in DE are highly dependent on the optimization problem. Therefore, one of the major issues with DE is the correct selection of the control parameters. A basic trend in DE research is the control parameter setting, which has been extensively studied in the literature (Eiben, Hinterding, & Michalewicz, 1999). The effect of the population size was reported in (Feoktistov & Janaqi, 2004).

Key Terms in this Chapter

Wireless Local Area Network (WLAN): A network in which a mobile user can connect to a local area network (LAN) through a wireless (radio) connection.

Sidelobe Level (SLL): The ratio, usually expressed in decibels (dB), of the amplitude at the peak of the main lobe to the amplitude at the peak of a side lobe.

WiMAX: A wireless communications standard designed to provide 30 to 40 megabit-per-second data rates.

Genetic Algorithms: A stochastic population-based global optimization technique that mimics the process of natural evolution.

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