Particle Swarm Optimization Algorithms Applied to Antenna and Microwave Design Problems

Sotirios K. Goudos, Zaharias D. Zaharis, Konstantinos B. Baltzis

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Particle Swarm Optimization (PSO) is an evolutionary optimization algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this chapter, the authors apply different PSO variants to common antenna and microwave design problems. The Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms are applied to real-valued optimization problems. Correspondingly, discrete PSO optimizers such as the binary PSO (binPSO) and the Boolean PSO with velocity mutation (BPSO-vm) are used to solve discrete-valued optimization problems. In case of a multi-objective optimization problem, the authors apply two multi-objective PSO variants. Namely, these are the Multi-Objective PSO (MOPSO) and the Multi-Objective PSO with Fitness Sharing (MOPSO-fs) algorithms. The design examples presented here include microwave absorber design, linear array synthesis, patch antenna design, and dual-band base station antenna optimization. The conclusion and a discussion on future trends complete the chapter.

Original languageEnglish
Title of host publicationSwarm Intelligence for Electric and Electronic Engineering
EditorsGirolamo Fornarelli, Luciano Mescia
PublisherIGI Global
Chapter6
Pages100-126
Number of pages27
ISBN (Electronic)9781466626973
ISBN (Print)9781466626669
DOIs
Publication statusPublished - 2013
Externally publishedYes

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