Evolution of Wire Antennas in Three Dimensions Using a Novel Growth Process

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.

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Evolution of Wire Antennas in Three Dimensions Using a Novel Growth Process

Cited by 4 publications

References 10 publications

Design and Evaluation of Curved Dipoles Antennas Optimum

Design and Evaluation of Curved Dipoles Antennas Optimum

Thinned Planar Array Design Using Boolean PSO With Velocity Mutation

Particle Swarm Optimization Algorithms Applied to Antenna and Microwave Design Problems

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