In recent years there has been an increasing attention to novel evolutionary optimization techniques employed to engineering and real-world applications. Among these, the design of antennas and electromagnetic devices is a well-established field of application. This paper presents a new population based algorithm inspired by the recent explosion of social networks and their capability to drive people's decision making process in everyday life. Early experimental studies have already proven its effectiveness in the optimized design of EM structures. Here this optimization procedure called SNO - Social Network Optimization is presented and tested in a first comparative study to prove its effectiveness and performance compared with traditional evolutionary algorithms.

Recently developed social-based algorithms for antennas optimization

GRIMACCIA, FRANCESCO;MUSSETTA, MARCO;NICCOLAI, ALESSANDRO;ZICH, RICCARDO
2014-01-01

Abstract

In recent years there has been an increasing attention to novel evolutionary optimization techniques employed to engineering and real-world applications. Among these, the design of antennas and electromagnetic devices is a well-established field of application. This paper presents a new population based algorithm inspired by the recent explosion of social networks and their capability to drive people's decision making process in everyday life. Early experimental studies have already proven its effectiveness in the optimized design of EM structures. Here this optimization procedure called SNO - Social Network Optimization is presented and tested in a first comparative study to prove its effectiveness and performance compared with traditional evolutionary algorithms.
2014 International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications, NEMO 2014
9781479928200
9781479928200
Evolutionary algorithms; Social Network Optimization; Mathematical Physics; Modeling and Simulation; Computational Mechanics; Electrical and Electronic Engineering; Theoretical Computer Science
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/968544
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