Application of Taguchi's Optimization Method and Self-Adaptive Differential Evolution to the Synthesis of Linear Antenna Arrays

Dib, Nihad; Goudos, Sotirios K.; Muhsen, Hani

doi:10.2528/pier09122306

Cited by 98 publications

(102 citation statements)

References 29 publications

(46 reference statements)

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“…Over the time-modulated linear array design instances we also compare the performance of MOEA/D-DE with that of two single-objective optimization techniques, namely DEGL (DE with Global and Local Neighborhood) [39] and CLPSO (Comprehensive Learning PSO) [40] that are the state-of-the-art variants of DE and PSO, two metaheuristic algorithms widely used in past for various electromagnetic optimization [2,4,26,[41][42][43][44]. For singleobjective optimization techniques, we use a weighted linear sum of the objective functions given in (5a)-(5c).…”

Section: Simulation Resultsmentioning

confidence: 99%

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Pal¹,

Das²,

Basak³

2010

PIER B

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Abstract-This article proposes a Multi-objective Optimization (MO) framework for the design of time-modulated linear antenna arrays with ultra low maximum Side Lobe Level (SLL), maximum Side Band Level (SBL) and main lobe Beam Width between the First Nulls (BWFN). In contrast to the existing optimization-based methods that attempt to minimize a weighted sum of SLL, SBL, and BWFN, we treat these as three distinct objectives that are to be achieved simultaneously and use one of the best known MultiObjective Evolutionary Algorithms (MOEAs) of current interest called MOEA/D-DE (Decomposition based MOEA with Differential Evolution operator) to determine the best compromise among these three objectives. Unlike the single-objective approaches, the MO approach provides greater flexibility in the design by yielding a set of equivalent final solutions from which the user can choose one that attains a suitable trade-off margin as per requirements. We compared time-modulated antenna structures with other methods for linear array synthesis such as the excitation method and the phase-position synthesis method on the basis of the approximated Pareto Fronts

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Section: Simulation Resultsmentioning

confidence: 99%

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Pal¹,

Das²,

Basak³

2010

PIER B

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“…High performance optimization algorithms (genetic or evolutionary type) can be included into the design process [19][20][21][22], in order to improve the power density of the studied machine. The analytical approach used for the designed machine is not presented here, the author willing to emphasize clearly the numerical and experimental results.…”

Section: The Stator Of the Proposed Pmsm6mentioning

confidence: 99%

Multi-Phase Synchronous Motor Solution for Steering Applications

Matyas¹,

Biro²,

Fodorean

2012

PIER

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Abstract-This paper presents the analysis of a six-phase permanent magnet synchronous machine (PMSM6) dedicated for electrical power steering system applications. The motor design is briefly described, as well as the construction of the studied motor. The study is validated by finite element method and via experimental results. Some simulated results prove the machine's capability to work even in faulty conditions. The operation of the machine was evaluated in generator and motor operation. In motor operation, the PMSM6 was fed based on scalar control.

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“…The individuals communicate and exchange information through natural processes like mutation, recombination and selection, to evolve increasingly fitter new individuals to a particular environment. More recently, differential evolution (DE) method proposed by Storn and Price [19][20][21], a simple and powerful global optimization algorithm, has attracted much attention due to its simplicity and less number of parameters to tune [22][23][24]. DE perturbs the currentgeneration population members with a scaled difference of randomly selected and distinct population members.…”

Section: Evolutionary Algorithms Backgroundmentioning

confidence: 99%

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

Mallipeddi¹,

Lie²,

Suganthan³

et al. 2011

PIER B

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Abstract-The presence of desired signal in the training data for sample covariance matrix calculation is known to lead to a substantial performance degradation, especially when the desired signal is the dominant signal in the training data. Together with the uncertainty in the look direction, most of the adaptive beamforming solutions are unable to approach the optimal performance. In this paper, we propose an evolutionary algorithm (EA) based robust adaptive beamforming that is able to achieve near optimal performance. The essence of the idea is to shape the array beam response such that it has maximum response in the desired signal's angular range and minimum response in the interferences' angular range. In addition, the approach introduces null-response constraints deduced from the array observation to achieve better interference cancelation performance. As a whole, the proposed optimization is solvable using an improved variant of the differential evolution (DE) algorithm. Numerical simulations are also presented to demonstrate the efficacy of the proposed algorithm.

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Application of Taguchi's Optimization Method and Self-Adaptive Differential Evolution to the Synthesis of Linear Antenna Arrays

Cited by 98 publications

References 29 publications

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Multi-Phase Synchronous Motor Solution for Steering Applications

Near Optimal Robust Adaptive Beamforming Approach Based on Evolutionary Algorithm

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