Binary problems are common in engineering and they can be suitably faced with Evolutionary Optimization. In the antenna field, these problems are quite common and they are characterized to be often multi-modal and non-convex, so they cannot be easily solved by means of standard optimization techniques. In particular, three different Evolutionary Algorithms have been frequently considered in recent years in the field of antenna arrays optimization, namely Stud-Genetic Algorithm (Stud-GA), binary Particle Swarm Optimization (bPSO) and Social Network Optimization (SNO). The aim of this paper is to extensively compare these three heuristics over standard benchmark functions and on a well-known antenna problem, i.e. the optimization of a thinned array. Numerical simulation will be conducted on an array of 121 elements and performances of the different approaches will be compared and validated over this real-world electromagnetic application.

Comparison of Binary Evolutionary Algorithms for Optimization of Thinned Array Antennas

Grimaccia, Francesco;Mussetta, Marco;Niccolai, Alessandro;Zich, Riccardo E.
2018

Abstract

Binary problems are common in engineering and they can be suitably faced with Evolutionary Optimization. In the antenna field, these problems are quite common and they are characterized to be often multi-modal and non-convex, so they cannot be easily solved by means of standard optimization techniques. In particular, three different Evolutionary Algorithms have been frequently considered in recent years in the field of antenna arrays optimization, namely Stud-Genetic Algorithm (Stud-GA), binary Particle Swarm Optimization (bPSO) and Social Network Optimization (SNO). The aim of this paper is to extensively compare these three heuristics over standard benchmark functions and on a well-known antenna problem, i.e. the optimization of a thinned array. Numerical simulation will be conducted on an array of 121 elements and performances of the different approaches will be compared and validated over this real-world electromagnetic application.
2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
9781509060177
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1079631
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