scholarly journals APPLICATION OF TAGUCHI'S OPTIMIZATION METHOD AND SELF-ADAPTIVE DIFFERENTIAL EVOLUTION TO THE SYNTHESIS OF LINEAR ANTENNA ARRAYS

2010 ◽  
Vol 102 ◽  
pp. 159-180 ◽  
Author(s):  
Nihad I. Dib ◽  
Sotirios K. Goudos ◽  
Hani Muhsen
Keyword(s):  
Differential Evolution ◽  
Antenna Arrays ◽  
Optimization Method ◽  
Linear Antenna ◽  
Adaptive Differential Evolution ◽  
Linear Antenna Arrays ◽  
Self Adaptive

scholarly journals Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5158
Author(s):  
Ruimeng Zhang ◽  
Yan Zhang ◽  
Jinping Sun ◽  
Qing Li
Keyword(s):  
Differential Evolution ◽  
Antenna Array ◽  
Antenna Arrays ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Linear Antenna ◽  
Pattern Synthesis ◽  
De Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Linear Antenna Arrays

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm’s performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.

scholarly journals PEMBOBOTAN KATA BERDASARKAN KLASTER PADA OPTIMISASI COVERAGE, DIVERSITY DAN COHERENCE UNTUK PERINGKASAN MULTI DOKUMEN

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Ryfial Azhar ◽  
Muhammad Machmud ◽  
Hanif Affandi Hartanto ◽  
Agus Zainal Arifin ◽  
Diana Purwitasari
Keyword(s):  
Differential Evolution ◽  
Text Analysis ◽  
Additional Term ◽  
Optimization Method ◽  
Term Weighting ◽  
High Coverage ◽  
Document Summarization ◽  
Adaptive Differential Evolution ◽  
Coherence Level ◽  
Self Adaptive

[Id]Peringkasan yang baik dapat diperoleh dengan coverage, diversity dan coherence yang optimal. Namun, terkadang sub-sub topik yang terkandug dalam dokumen tidak terekstrak dengan baik, sehingga keterwakilan setiap sub-sub topik tersebut tidak ada dalam hasil peringkasan dokumen. Pada paper ini diusulkan metode baru pembobotan kata berdasarkan klaster pada optimisasi coverage, diversity dan coherence untuk peringkasan multi-dokumen. Metode optimasi yang digunakan ialah self-adaptive differential evolution (SaDE) dengan penambahan pembobotan kata berdasarkan hasil dari pembentukan cluster dengan metode Similarity Based Histogram Clustering (SHC). Metode SHC digunakan untuk mengklaster kalimat sehingga setiap sub-topik pada dokumen bisa terwakili dalam hasil peringkasan. Metode SaDE digunakan untuk mencari solusi hasil ringkasan yang memiliki tingkat coverage, diversity, dan coherence paling tinggi. Uji coba dilakukan pada 15 topik dataset Text Analysis Conference (TAC) 2008. Hasil uji coba menunjukkan bahwa metode yang diusulkan dapat menghasilkan ringkasan skor ROUGE-1 sebesar 0.6704, ROUGE-2 sebesar 0.2051, ROUGE-L sebesar 0.6271 dan ROUGE-SU sebesar 0.3951.Kata kunci : peringkasan multi dokumen, similarity based histogram clustering, coverage, diversity, coherence[En]Good summary can be obtained with optimizing coverage, diversity, and coherence. Nevertheless, sometime sub-topics wich is contained in the document is not extracted well, so that the representation of each sub-topic is appear in docment summarizarion result. In this paper, we propose new of term weighting based on? cluster in optimizing coverage, diversity, and coherence for multi-document summarization. Optimization method which is used is self-adaptive differential evolution (SaDE) with additional term weighting based on clustering result with Similarity Based Histogram Clustering (SHC). SHC is used to cluster sentence so that every sub-topic in the document can be represented in summarization result. SaDE is used to search summarization result solution which has high coverage, diversity, and coherence level. Experiment is done on 15 topics in Text Analysis Conference (TAC) 2008 dataset. Experimental results show that this proposed method can produce summarization score? ROUGE-1 0.6704, ROUGE-2 0.2051, ROUGE-L 0.6271 and ROUGE-SU 0.3951.Keywords: multy-document summarization, similarity based histogram clustering, coverage, diversity, coherence.

A Multi-Objective Approach to Subarrayed Linear Antenna Arrays Design Based on Memetic Differential Evolution

2013 ◽  
Vol 61 (6) ◽  
pp. 3042-3052 ◽  
Author(s):  
Sotirios K. Goudos ◽  
Konstantinos A. Gotsis ◽  
Katherine Siakavara ◽  
Elias E. Vafiadis ◽  
John N. Sahalos
Keyword(s):  
Differential Evolution ◽  
Antenna Arrays ◽  
Linear Antenna ◽  
Multi Objective ◽  
Linear Antenna Arrays

scholarly journals Optimum design of non-uniform symmetrical linear antenna arrays using a novel modified invasive weeds optimization

2016 ◽  
Vol 65 (1) ◽  
pp. 5-18 ◽  
Author(s):  
El Hadi Kenane ◽  
Farid Djahli
Keyword(s):  
Antenna Arrays ◽  
Optimization Technique ◽  
Optimization Method ◽  
Good Alternative ◽  
Side Lobe ◽  
Good Choice ◽  
Side Lobe Level ◽  
Invasive Weeds ◽  
Linear Antenna ◽  
Linear Antenna Arrays

Abstract This paper presents a new modified method for the synthesis of non-uniform linear antenna arrays. Based on the recently developed invasive weeds optimization technique (IWO), the modified invasive weeds optimization method (MIWO) uses the mutation process for the calculation of standard deviation (SD). Since the good choice of SD is particularly important in such algorithm, MIWO uses new values of this parameter to optimize the spacing between the array elements, which can improve the overall efficiency of the classical IWO method in terms of side lobe level (SLL) suppression and nulls control. Numerical examples are presented and compared to the existing array designs found in the literature, such as ant colony optimization (ACO), particle swarm optimization (PSO), and comprehensive learning PSO (CLPSO). Results show that MIWO method can be a good alternative in the design of non-uniform linear antenna array.

Sign in / Sign up

Export Citation Format

Share Document