Self-Adaptive Differential Evolution Applied to Real-Valued Antenna and Microwave Design Problems

被引：100

作者：

Goudos, Sotirios K. ^{[1
]}

Siakavara, Katherine ^{[1
]}

Samaras, Theodoros ^{[1
]}

Vafiadis, Elias E. ^{[1
]}

Sahalos, John N. ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Radiocommun Lab, Dept Phys, GR-54124 Thessaloniki, Greece

来源：

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION | 2011年 / 59卷 / 04期

关键词：

Differential evolution (DE); evolutionary algorithms (EAs); linear array synthesis; microwave filter design; optimization methods; particle swarm optimization (PSO); patch antenna design; PARTICLE SWARM OPTIMIZATION; ELECTROMAGNETIC INVERSE SCATTERING; PATTERN SYNTHESIS; STRATEGY; ARRAYS; PSO; RECONSTRUCTION; PARAMETERS; MULTIBAND; GEOMETRY;

D O I：

10.1109/TAP.2011.2109678

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Particle swarm optimization (PSO) is an evolutionary algorithm based on the bird fly. Differential evolution (DE) is a vector population based stochastic optimization method. The fact that both algorithms can handle efficiently arbitrary optimization problems has made them popular for solving problems in electromagnetics. In this paper, we apply a design technique based on a self-adaptive DE (SADE) algorithm to real-valued antenna and microwave design problems. These include linear-array synthesis, patch-antenna design and microstrip filter design. The number of unknowns for the design problems varies from 6 to 60. We compare the self-adaptive DE strategy with popular PSO and DE variants. We evaluate the algorithms' performance regarding statistical results and convergence speed. The results obtained for different problems show that the DE algorithms outperform the PSO variants in terms of finding best optima. Thus, our results show the advantages of the SADE strategy and the DE in general. However, these results are considered to be indicative and do not generally apply to all optimization problems in electromagnetics.

引用

页码：1286 / 1298

页数：13

共 63 条

[11]

CLERC M, 1999, C EV COMP WASH DC

[12] Application of a parallel particle swarm optimization scheme to the design of electomagnetic absorbers [J].

Cui, SM ;

Weile, DS .

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2005, 53 (11) :3616-3624

[13]

Das S, 2008, STUD COMPUT INTELL, V116, P1, DOI 10.1007/978-3-540-78297-1_1

[14] Beamforming in the Presence of Mutual Coupling Based on Constrained Particle Swarm Optimization [J].

Demarcke, Pieterjan ;

Rogier, Hendrik ;

Goossens, Roald ;

De Jaeger, Peter .

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2009, 57 (06) :1655-1666

[15]

Di Pillo G., 1994, ALGORITHMS CONTINUOU, P1

[16] Design and optimization of multi-band Wilkinson power divider [J].

Dib, Nihad ;

Khodier, Majid .

INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, 2008, 18 (01) :14-20

[17] Computational approach based on a particle swarm optimizer for microwave imaging of two-dimensional dielectric scatterers [J].

Donelli, M ;

Massa, A .

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2005, 53 (05) :1761-1776

[18] A Hybrid Approach Based on PSO and Hadamard Difference Sets for the Synthesis of Square Thinned Arrays [J].

Donelli, Massimo ;

Martini, Anna ;

Massa, Andrea .

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2009, 57 (08) :2491-2495

[19]

FEKO, 2003, US MAN

[20] A robust technique for well-log data inversion [J].

Goswami, JC ;

Mydur, R ;

Wu, P ;

Heliot, D .

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2004, 52 (03) :717-724

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