Differential evolution for RFID antenna design: A comparison with ant colony optimisation

James Montgomery, Marcus Randall, Andrew Lewis

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

12 Citations (Scopus)

Abstract

Differential evolution (DE) has been traditionally applied to solving benchmark continuous optimisation functions. To enable it to solve a combinatorially oriented design problem, such as the construction of effective radio frequency identification antennas, requires the development of a suitable encoding of the discrete decision variables in a continuous space. This study introduces an encoding that allows the algorithm to construct antennas of varying complexity and length. The DE algorithm developed is a multiobjective approach that maximises antenna efficiency and minimises resonant frequency. Its results are compared with those generated by a family of ant colony optimisation (ACO) meta-heuristics that have formed the standard in this area. Results indicate that DE can work well on this problem and that the proposed solution encoding is suitable. On small antenna grid sizes (hence, smaller solution spaces) DE performs well in comparison to ACO, while as the solution space increases its relative performance decreases. However, as the ACO employs a local search operator that the DE currently does not, there is scope for further improvement to the DE approach.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11
Place of Publicationonline
PublisherSpecial Interest Group on Genetic and Evolutionary Computation
Pages673-680
Number of pages8
ISBN (Print)9781450305570
DOIs
Publication statusPublished - 2011
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Dublin, Ireland
Duration: 12 Jul 201116 Jul 2011

Conference

Conference13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
CountryIreland
CityDublin
Period12/07/1116/07/11

Fingerprint

Ant colony optimization
Radio Frequency Identification
Differential Evolution
Radio frequency identification (RFID)
Antenna
Antennas
Encoding
Small Solutions
Continuous Optimization
Resonant Frequency
Differential Evolution Algorithm
Mathematical operators
Natural frequencies
Metaheuristics
Local Search
Maximise
Design
Benchmark
Grid
Minimise

Cite this

Montgomery, J., Randall, M., & Lewis, A. (2011). Differential evolution for RFID antenna design: A comparison with ant colony optimisation. In Genetic and Evolutionary Computation Conference, GECCO'11 (pp. 673-680). online: Special Interest Group on Genetic and Evolutionary Computation. https://doi.org/10.1145/2001576.2001669
Montgomery, James ; Randall, Marcus ; Lewis, Andrew. / Differential evolution for RFID antenna design : A comparison with ant colony optimisation. Genetic and Evolutionary Computation Conference, GECCO'11. online : Special Interest Group on Genetic and Evolutionary Computation, 2011. pp. 673-680
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Montgomery, J, Randall, M & Lewis, A 2011, Differential evolution for RFID antenna design: A comparison with ant colony optimisation. in Genetic and Evolutionary Computation Conference, GECCO'11. Special Interest Group on Genetic and Evolutionary Computation, online, pp. 673-680, 13th Annual Genetic and Evolutionary Computation Conference, GECCO'11, Dublin, Ireland, 12/07/11. https://doi.org/10.1145/2001576.2001669

Differential evolution for RFID antenna design : A comparison with ant colony optimisation. / Montgomery, James; Randall, Marcus; Lewis, Andrew.

Genetic and Evolutionary Computation Conference, GECCO'11. online : Special Interest Group on Genetic and Evolutionary Computation, 2011. p. 673-680.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Montgomery J, Randall M, Lewis A. Differential evolution for RFID antenna design: A comparison with ant colony optimisation. In Genetic and Evolutionary Computation Conference, GECCO'11. online: Special Interest Group on Genetic and Evolutionary Computation. 2011. p. 673-680 https://doi.org/10.1145/2001576.2001669