Mustafa SeçmenM. Fatih TasgetirenSecmen, MustafaTasgetiren, Mehmet Fatih2025-10-06201317518792, 175187841751-87841751-879210.1049/iet-rsn.2012.02122-s2.0-84883423750https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883423750&doi=10.1049%2Fiet-rsn.2012.0212&partnerID=40&md5=d095a30b0d5ca6b05b87ae5d30ba3c54https://gcris.yasar.edu.tr/handle/123456789/10081https://doi.org/10.1049/iet-rsn.2012.0212In this study an ensemble of differential evolution (DE) algorithms is presented to classify electromagnetic targets in resonance scattering region. The algorithm aims to synthesize a special incident signal for each target which is defined as the main discrimination feature in the given target recognition method. In the proposed algorithm the amplitudes of basis functions and the duration of this incident signal are optimised to give minimum late-time scattered signal's energy which is the main fitness function of the algorithm. The proposed DE algorithm is applied to a target set consisting of lossless dielectric spheres and correct recognition rates for both noiseless and noisy signals are obtained. The results for both developed DE algorithm and other DE variants of traditional DE adaptive differential evolution with optional external archive (JADE) jDE are also given to compare the algorithms and show the effectiveness of the proposed one. © The Institution of Engineering and Technology 2013. © 2013 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessAdaptive Differential Evolutions, Differential Evolution Algorithms, Electromagnetic Target Recognition, Fitness Functions, Lossless Dielectrics, Resonance Scattering, Scattered Signals, Target Recognition, Silicate Minerals, Evolutionary AlgorithmsAdaptive differential evolutions, Differential evolution algorithms, Electromagnetic target recognition, Fitness functions, Lossless dielectrics, Resonance scattering, Scattered signals, Target recognition, Silicate minerals, Evolutionary algorithmsArticle